GfKl 2008 - Legos

GfKl 2008
German Classification Society
32nd Annual Conference
Advances in Data Analysis, Data Handling
and Business Intelligence
Joint Conference with the
British Classification Society (BCS) and the
Dutch/Flemish Classification Society (VOC)
July 16−18, 2008
Hamburg
Program and
Abstract Volume
http://gfkl2008.hsu-hh.de

Main contact:
Prof. Dr. Wilfried Seidel
Helmut-Schmidt-University
Holstenhofweg 85
22043 Hamburg
Germany
+49 (0)40 6541 2315
gfkl2008@hsu-hh.de
− ii −

Contents
Welcome, v
Sponsors, vi
Scientific Program Committee, vii
Organizing Committee, vii
Plenary and Semi-plenary Lectures, viii
Invited Sessions, x
Detailed Schedule, xi
List of Contributions, xxviii
Author Index, xxxvii
Abstracts, 1
− iii −

− iv −

Welcome
On behalf of the Helmut-Schmidt-University Hamburg,
we welcome you to GfKl2008 − Advances in Data Analysis,
Data Handling and Business Intelligence − the 32nd Annual
Conference of the German Classification Society, organized
in cooperation with the British Classification Society (BCS)
and the Dutch/Flemish Classification Society (VOC).
The conference features 13 invited lectures (3 plenary
speeches and 10 semi-plenary lectures), 166 contributed
talks, 4 invited sessions, and 2 workshops.
We are indebted to those who suggested and supported
having GfKl2008 held in Hamburg. We are grateful to those
who volunteered in the conference organization and we
acknowledge the generous financial backing by our sponsors.
We wish you a very pleasant and stimulating GfKl2008.
Claudia Fantapié Altobelli
Andreas Fink
Hartmut Hebbel
Wilfried Seidel
Detlef Steuer
Ulrich Tüshaus
(Organizing committee,
Helmut-Schmidt-University Hamburg)
Berthold Lausen
Alfred Ultsch
(Co-chairs of the program committee)
− v −

Sponsors
The organizers would like to express their appreciation to the
following organizations for providing financial help and other
support:
Deutsche
Forschungsgemeinschaft
Hamburg-Mannheimer
Versicherungen
Springer-Verlag
Vattenfall
Gesellschaft für
Konsumforschung
Hamburger Sparkasse
Statsoft GmbH
Volksfürsorge Deutsche
Lebensversicherung AG
− vi −

Scientific Program Committee
H.-H. Bock (RWTH Aachen)
R. Decker (Uni Bielefeld)
V. Esposito Vinzi (ESSEC Paris)
W. Esswein (TU Dresden)
C. Fantapié Altobelli (HSU Hamburg)
A. Fink (HSU Hamburg)
W. Gaul (Uni Karlsruhe)
H. Hebbel (HSU Hamburg)
Ch. Hennig (Uni London)
K. Jajuga (Wroclaw Univ. of Economics)
H.-P. Klenk (DSMZ Braunschweig)
B. Lausen (Uni Erlangen-Nürnberg, Co-Chair)
H. Locarek-Junge (TU Dresden)
F. Murtagh (Uni London)
A. Okada (Uni Tokyo)
L. Schmidt-Thieme (Uni Hildesheim)
W. Seidel (HSU Hamburg)
D. Steuer (HSU Hamburg)
U. Tüshaus (HSU Hamburg)
A. Ultsch (Uni Marburg, Co-Chair)
M. van de Velden (Uni Rotterdam)
D. van den Poel (Uni Ghent)
I. van Mechelen (Uni Leuven)
R. Wehrens (Uni Nijmegen)
C. Weihs (Uni Dortmund)
Organizing Committee
Claudia Fantapié Altobelli
Andreas Fink
Hartmut Hebbel
Wilfried Seidel
Detlef Steuer
Ulrich Tüshaus
− vii −

Plenary and Semi-plenary Lectures
Wednesday, July 16, 10:00–10:45:
Walter Radermacher, President Federal Statistical Office of
Germany, Wiesbaden, Germany
“Statistical Processes Under Change – Enhancing Data Quality
with Pretests” (Room 5)
Wednesday, July 16, 11:00–11:40:
Geoffrey John McLachlan, University of Brisbane, Australia
“Clustering of High-Dimensional Data Via Finite Mixture Models”
(Room 5)
Fred Hamprecht, University Heidelberg, Germany
“Segmentation of Neural Tissue” (Room 3)
Wednesday, July 16, 14:00–14:45:
Bernhard Schölkopf, Max-Planck-Institute, Tübingen, Germany
“Machine Learning Applications of Positive Definite Kernels”
(Room 5)
Thursday, July 17, 09:00–09:40:
Patrick Groenen, University Rotterdam, The Netherlands
“Support Vector Machines in the Primal using Majorization and
Kernels” (Room 5)
Gilles Bisson, La Tronche, France
“Clustering of Molecules and Structured Data” (Room 3)
− viii −

Thursday, July 17, 15:55–16:35:
Sabine Krolak-Schwerdt, University Wuppertal, Germany
“Strategies of Model Construction for the Analysis of Judgment
Data” (Room 3)
Gilles Celeux, INRIA, France
“Choosing the Number of Clusters in the Latent Class Model”
(Room 5)
Friday, July 18, 09:00–09:40:
Francesco Palumbo, University of Macerata, Italy
“Clustering and Dimensionality Reduction to Discover Interesting
Patterns in Binary Data” (Room 5)
Raimund Wildner, GfK, Nürnberg, Germany
“Management and Methods: How to do Market Segmentation
Projects” (Room 3)
Friday, July 18, 11:20–12:00:
Adi Ben-Israel, Rutgers University, USA
“Probabilistic Distance Clustering” (Room 5)
Tagashi Imaizumi, Tama University Tokyo, Japan
“Dimensionality Reduction of Similarity Matrix” (Room 3)
Friday, July 18, 13:15–14:00:
Fred R. McMorris, Illinois Institute of Technology, USA
“Majority-rule Consensus: From Preferences (Social Choice) to
Trees (Biology and Classication Theory)” (Room 5)
− ix −

Invited Sessions
Wednesday, July 16, 14:50–16:05 (Room 2)
VOC
(Chairs: van de Velden, Wehrens)
Wednesday, July 16, 16:50–18:30 (Room 2)
PLS Path Modeling
(Chair: Esposito Vinzi)
Thursday, July 17, 09:45–11:00 (Room 2)
Microarrays in Clinical Research
(Chairs: Lausen, Ultsch)
Thursday, July 17, 14:00–15:40 (Room 2)
BCS
(Chairs: Hennig, Murtagh)
− x −

Detailed Schedule
13:30-
17:30
Tuesday July 15, 2008 page
Pre-conference Workshop Room 4
Lenz, Hans-J. Data Quality: Defining, Measuring and
Improving
20:00 Informal Get Together (Hotel Baseler Hof, Esplanade 11)
09:00-
10:00
Wednesday July 16, 2008
Opening Ceremony (Chair: Seidel) Room
5
09:00 Welcome Claus Weihs (President of GfKl)
Wilfried Seidel (Local Organizers)
09:05 Welcome Herlind Gundelach
(Senator for Science and
Research, State Hamburg)
09:15 Welcome Hans Christoph Zeidler
(President of Helmut-Schmidt-
University Hamburg)
09:30 GfKl Best Paper
Award 2007:
Presentation and
Laudation
Claus Weihs (President of GfKl)
N.N.
09:50 Program Overview Berthold Lausen, Alfred Ultsch
(Co-Chairs Program Committee)
10:00-
10:45
Plenary Lecture (Chair: Seidel)
Radermacher, Walter Statistical processes under
change − Enhancing data quality
with pretests
− xi −
page
118
Room
5

10:00-
18:00
10:45-
11:00
11:00-
11:40
Workshop: Libraries (see separate schedule) Room
403
Coffee
Semi-plenary Lectures
McLachlan, Geoffrey
John
Hamprecht, Fred A.
et al.
Session Mixture Analysis I:
Testing
11:45-
12:10
12:10- Holzmann, Hajo;
12:35 Dannemann, Jörn
12:35-
13:00
Clustering of High-Dimensional
Data Via Finite Mixture Models
(Chair: McMorris)
Segmentation of Neural Tissue
(Chair: Wehrens)
93 Room
5
5 Room
3
(Chair: Seidel)
49
Room
3
Gassiat, Elisabeth Likelihood ratio test for general
mixture models
Likelihood ratio testing for hidden
Markov models
Pommeret, Denys Testing distribution in errors in
variables models
Session Pattern Recognition and
Machine Learning I
11:45-
12:10
12:10-
12:35
12:35-
13:00
Haasdonk, Bernard;
Pekalska, Elzbieta
Louw, Nelmarie;
Lamont, Morne; Steel,
Sarel
Classification with Regularized
Kernel Mahalanobis-Distances
Identifying Atypical Cases in
Kernel Fisher Discriminant
Analysis by using the Smallest
Enclosing Hypersphere
Trzesiok, Michal Relevant Importance of Predictor
Variables in Support Vector
Machines Models
68
113
(Chair: Groenen) Room
405/6
Session Collective Intelligence (Chair: Geyer-Schulz) Room
101/3
11:45-
12:10
Geyer-Schulz, Andreas;
Hoser, Bettina
The Potential of Social Intelligence
for Collective Intelligence
− xii −
57
87
152
51

12:10-
12:35
12:35-
13:00
Mylonas, Phivos;
Solachidis, Vassilios;
Geyer-Schulz, Andreas;
Hoser, Bettina;
Chapman, Sam;
Ciravegna, Fabio;
Staab, Stefen; Smrz,
Pavel; Kompatsiaris,
Yiannis; Avrithis,
Yannis
Solachidis, Vassilios;
Mylonas, Phivos;
Geyer-Schulz, Andreas;
Hoser, Bettina;
Chapman, Sam;
Ciravegna, Fabio;
Staab, Stefen;
Contopoulos, Costis;
Gkika, Ioanna; Smrz,
Pavel; Kompatsiaris,
Yiannis; Avrithis,
Yannis
Session Evaluation of Clustering
Algorithms and Data
Structures
11:45- Kaiser, Sebastian;
12:10 Leisch, Friedrich
12:10-
12:35
12:35-
13:00
Bade, Korinna; Benz,
Dominik
Grün, Bettina; Leisch,
Friedrich
Session Genome and DNA
Analysis
11:45-
12:10
12:10-
12:35
12:35-
13:00
Efficient Media Exploitation
towards Collective Intelligence
100
Generating Collective Intelligence 137
(Chair: Leisch) Room
2
Benchmarking Bicluster
Algorithms
Evaluation Strategies for Learning
Algorithms of Hierarchical
Structures
Model diagnostics of finite
mixtures using bootstrapping
Klenk, Hans-Peter Polyphasic genomic approach for
the taxonomy of archaea and
bacteria
Huson, Daniel H.;
Rupp, Regula
75
8
56
(Chair: Klenk) Room
6
Using Cluster Networks to
Represent Non-Compatible Sets of
Clusters
Hütt, Marc-Thorsten Genome phylogeny based on
short-range correlations in DNA
sequences
− xiii −
78
71
72

Session Market Risk and Credit
Risk
11:45-
12:10
12:10-
12:35
12:35-
13:00
13:00-
14:00
14:00-
14:45
Bravo, Cristian;
Maldonado, Sebastian;
Weber, Richard
(Chair: Locarek-Junge) Room
4
Practical experiences from Credit
Scoring projects for Chilean
financial organizations
21
Kuziak, Katarzyna An application of copula functions
to market risk management
Rokita, Pawel; Piontek,
Krzysztof
Lunch (and Meetings)
Extreme unconditional
dependence vs. multivariate
GARCH effect in the analysis of
dependence between high losses
on Polish and German stock
indexes
Plenary Lecture (Chair: Lausen)
Schölkopf, Bernhard Machine Learning applications of
positive definite kernels
Session Mixture Analysis II:
Clustering and
Classification
14:50-
15:15
15:15-
15:40
15:40-
16:05
16:05-
16:30
Pons, Odile Classification with an increasing
number of components
Lukociene, Olga;
Vermunt, Jeroen K.
Calò, Daniela G.; Viroli,
Cinzia
Latouche, Pierre J.;
Ambroise, Christophe;
Birmelé, Etienne
Session Pattern Recognition and
Machine Learning II
14:50-
15:15
15:15-
15:40
15:40-
16:05
Stecking, Ralf;
Schebesch, Klaus B.
Huellermeier, Eyke;
Vanderlooy, Stijn
Hühn, Jens;
Hüllermeier, Eyke
83
121
132 Room
5
(Chair: Montanari) Room
3
Determining the number of
components in mixture models for
hierarchical data
Visualizing data in Gaussian
mixture model classification
Bayesian Methods for Graph
Clustering
114
90
24
85
(Chair: Nalbantov) Room
405/6
Generating Fictitious Training Data
for Credit Client Classification
Combining Predictions in Pairwise
Classification: An Adaptive Voting
Strategy and Its Relation to
Weighted Voting
Rule-Based Learning of Reliable
Classifiers
− xiv −
140
70
69

Session Invited Session: VOC (Chairs: van de Velden, Wehrens) Room
2
14:50-
15:15
15:15-
15:40
15:40-
16:05
Timmerman, Marieke
E.; Lichtwarck-Aschoff,
Anna; Ceulemans, Eva
van der Heijden, Peter
G.M.
van der Ark, Andries L.;
Straat, J. Hendrik
Session Ensemble Methods and
Other Subjects
14:50-
15:15
15:15-
15:40
15:40-
16:05
16:05-
16:30
Multilevel Simultaneous
Component Analysis for Studying
Inter-individual and Intraindividual
Variabilities
Estimating the prevalence of rule
transgression
Selection of items for tests and
questionnaires using Mokken scale
analysis
149
158
157
(Chair: Boulesteix) Room
6
Häberle, Lothar On classification of species of
representation rings
58
Strobl, Carolin; Zeileis, A New, Conditional Variable 143
Achim
Importance Measure for Random
Forests
Potapov, Sergej;
Lausen, Berthold
Bagging with different split criteria 115
Adler, Werner;
Classification of Paired Data Using 4
Brenning, Alexander;
Lausen, Berthold
Ensemble Methods
Session Market Risk and Credit
Risk
14:50-
15:15
15:15-
15:40
15:40-
16:05
16:05-
16:30
16:30-
16:50
(Chair: Locarek-Junge) Room
4
Piontek, Krzysztof The Analysis of the power for
some chosen VaR backtesting
procedures - simulation approach
Koralun-Bereznicka,
Julia
Dias, José G.; Vermunt,
Jeroen K.; Ramos, Sofia
Sardet, Laure; Patilea,
Valentin
Coffee
Multivariate comparative analysis
of stock exchanges - the European
perspective
Mixture Hidden Markov Models in
Finance Research
Beta-kernel density estimation
using mixture-based
transformations: an application to
claims distribution
− xv −
112
81
33
125

Session Mixture Analysis III:
Model Fitting, Estimation
and Applications
16:50-
17:15
17:15-
17:40
17:40-
18:05
18:05-
18:30
18:30-
18:55
Greselin, Francesca;
Ingrassia, Salvatore
Neykov, Neyko;
Filzmoser, Peter;
Neytchev, Plamen
Garel, Bernard;
Boucharel, Julien;
Dewitte, Boris; du
Penhoat, Yves
(Chair: McLachlan) Room
3
A note on constrained EM
algorithms for mixtures of
elliptical distributions
Robust fitting of mixtures: The
approach based on the Trimmed
Likelihood Estimator
Non-Gaussian nature of ENSO
signals and climate shifts: implications
for regional studies off the
western coast of South America
Schlattmann, Peter Comparison of four estimators of
the heterogeneity variance for
meta-analysis
Schiffner, Julia; Weihs,
Claus
Session Pattern Recognition and
Machine Learning III
16:50-
17:15
17:15-
17:40
17:40-
18:05
18:05-
18:30
Localized Classification Using
Mixture Models
53
103
48
131
130
(Chair: Hüllermeier) Room
405/6
Wehrens, Ron Supervised Self-Organising Maps
and More
160
Worm, Katja; Meffert, Image Based Mail Piece
166
Beate
Identification using Unsupervised
Learning
Barbosa, Rui Pedro;
Belo, Orlando
Autonomous Forex Trading Agents 9
Chiou, Hua-Kai; Huang, Applying Rough Set Theory to 27
Yong-Ting; Liu, Gia- Constructing Knowledge Base for
Shie
Critical Military Commodity
Management
Session Invited Session: PLS
Path Modeling
16:50-
17:15
17:15-
17:40
17:40-
18:05
(Chair: Esposito Vinzi) Room
2
Ringle, Christian FIMIX-PLS Segmentation of Data
for Path Models with Multiple
Endogenous LVs
Trinchera, L.; Esposito
Vinzi, Vincenzo
A Comprehensive Partial Least
Squares Approach to Component-
Based Structural Equation
Modeling
Henseler, Jörg Nonlinear Effects in PLS Path
Models: A Comparison of Available
Approaches
− xvi −
120
151
63

18:05-
18:30
Betzin, Jörg Categorical Data in PLS Path
modeling
Session Microarray Data
Analysis
16:50-
17:15
17:15-
17:40
17:40-
18:05
18:05-
18:30
Boulesteix, Anne-
Laure; Slawski, Martin
Slawski, Martin;
Boulesteix, Anne-
Laure; Daumer, Martin
Scharl, Theresa;
Leisch, Friedrich
Martin-Magniette,
Marie-Laure; Mary-
Huard, Tristan; Bérard,
Caroline; Robin,
Stéphane
Session Investments and
Capital Markets
16:50-
17:15
17:15-
17:40
17:40-
18:05
18:05-
18:30
Locarek-Junge,
Hermann; Mihm, Max
14
(Chair: Benner) Room
6
On optimistic bias in reporting
microarray-based classification
accuracy
20
'CMA' - Steps in developing a
comprehensive R-toolbox for
classification with microarray data
and other high-dimensional
problems
136
Quality-Based Clustering of
Functional Data: Applications to
Time Course Microarray Data
127
ChIPmix : Mixture model of
regressions for ChIP-chip
experiment analysis
92
(Chair: Ultsch) Room
4
Fundamental Indexation - testing
the concept in the German stock
market
86
Ultsch, Alfred Is log ratio a good value for
measuring return in stock
investments?
Klein, Christian;
Kundisch, Dennis
Bessler, Wolfgang;
Holler, Julian
Index-Based Investment Vehicles
- A Comparative Study for the
German DAX
Hedge Funds in a Bayesian Asset
Allocation Framework:
Incorporating Information on
market states and manager's
ability
19:00 Reception (Building “M1”)
− xvii −
154
77
13

09:00-
09:40
09:45-
17:00
Thursday July 17, 2008
Semi-plenary Lectures
Groenen, Patrick J.F.
et al.
Support Vector Machines in the
Primal using Majorization and
Kernels (Chair: Okada)
Bisson, Gilles Clustering of molecules and
structured data (Chair: Hennig)
Workshop: Decimal
Classification
Session Clustering and
Classification I
9:45-
10:10
10:10-
10:35
10:35-
11:00
Godehardt, Erhard;
Jaworski, Jerzy;
Rybarczyk, Katarzyna
54 Room
5
16 Room
3
(see separate schedule) Room
403
(Chair: Bock) Room
3
Isolated vertices in random
intersection graphs
52
Rozmus, Dorota Cluster ensemble based on cooccurrence
data
Enyukov, Igor Regression-autoregression based
clustering
Session Bayesian, Neural, and
Fuzzy Clustering I
9:45-
10:10
10:10-
10:35
10:35-
11:00
Borgelt, Christian Weighting and Selecting Features
in Fuzzy Clustering
Neumann, Anneke;
Ambrosi, Klaus; Hahne,
Felix
Winkler, Roland; Rehm,
Frank; Kruse, Rudolf
Session Invited Session:
Microarrays in Clinical
Research
9:45-
10:25
10:25-
11:00
Approach for Dynamic Problems in
Clustering
Clustering with Repulsive
Prototypes
123
37
(Chair: Kruse) Room
405/6
18
102
163
(Chairs: Lausen, Ultsch) Room
2
Ultsch, Alfred Comparison of Algorithms to find
differentially expressed Genes in
Microarray Data
Hielscher, Thomas;
Zucknick, Manuela;
Werft, Wiebke; Benner,
Axel
On the prognostic value of gene
expression signatures for
censored data
− xviii −
153
67

Session Statistical Musicology I (Chair: Weihs) Room
6
9:45- Eigenfeldt, Arne; Multimodal Performance Analysis 35
10:10 Kapur, Ajay
of Electronic Sitar
10:10- Sommer, Katrin; Analysis of polyphonic musical 138
10:35 Weihs, Claus
time series
10:35-
11:00
Desmet, Frank Michel;
Leman, Marc; Lesaffre,
Micheline
Session Marketing and
Management Science I
9:45-
10:10
10:10-
10:35
10:35-
11:00
11:00-
11:20
Wagner, Ralf;
Sauerwald, Erik
Sagan, Adam;
Kowalska-Musial,
Magdalena
Becker, Niels; Werners,
Brigitte
Coffee
Session Clustering and
Classification II
11:20-
11:45
11:45-
12:10
12:15-
12:45
Nugent, Rebecca;
Stuetzle, Werner
Herrmann, Lutz;
Ultsch, Alfred
Software Presentation
Eichenberg, Thilo
(StatSoft)
Session Bayesian, Neural, and
Fuzzy Clustering II
11:20-
11:45
11:45-
12:10
12:10-
12:35
Gabriel, Thomas R.;
Thiel, Kilian; Berthold,
Michael R.
Fritsch, Arno; Ickstadt,
Katja
Steinbrecher, Matthias;
Kruse, Rudolf
Statistical analysis of human body
movement and group interactions
in response to music
32
(Chair: van den Poel) Room
4
Clustering Consumers with
Respect to Their Marketing
Reactance Behavior
159
Dyadic Interactions in Service
Encounter - Bayesian SEM
Approach
124
Improving Product Line Design
with Bundling
10
(Chair: Vichi) Room
3
Cluster Tree Estimation using a
Generalized Single Linkage
Method
104
Strengths and Weaknesses of Ant
Colony Clustering
65
STATISTICA
Multi-Dimensional Scaling applied
to Hierarchical Fuzzy Rule
Systems
An Improved Criterion for
Clustering Based on the Posterior
Similarity Matrix
Clustering Association Rules with
Fuzzy Concepts
− xix −
Room
3
(Chair: Kruse) Room
405/6
45
43
141

Session Text Mining (Chair: Schmidt-Thieme) Room
101/3
11:20-
11:45
11:45-
12:10
12:10-
12:35
12:35-
13:00
Karatzoglou,
Alexandros; Feinerer,
Ingo; Hornik, Kurt
Schierle, Martin;
Trabold, Daniel
Hermes, Jürgen;
Schwiebert, Stephan
Nonparametric distribution
analysis for text mining
Multilingual knowledge based
concept recognition in textual data
Classification of text processing
components: The Tesla Role
System
Thorleuchter, Dirk Mining ideas from textual
information
Session Modelling Exchange
from Archaeological
Evidence
11:20-
11:45
11:45-
12:10
12:10-
12:35
Schyle, Daniel The Late Neolithic flint axe
production on the Lousberg
(Aachen, Germany) – An
extrapolation of supply and
demand and population density
Dolata, Jens; Mucha,
Hans-Joachim; Bartel,
Hans-Georg
Mapping Findspots of Roman
Military Brickstamps in
Mogontiacum (Mainz) and
Archaeometrical Analysis
Herzog, Irmela Reconstructing Central Places and
Settlements Groups
76
128
64
147
(Chair: Kerig) Room
2
Session Statistical Musicology II (Chair: Weihs) Room
6
11:20-
11:45
11:45-
12:10
12:10-
12:35
Meyer, Florian; Ultsch,
Alfred
Lukashevich, Hanna;
Dittmar, Christian;
Bastuck, Christoph
Finding Music Fads by clustering
Online Radio Data with Emergent
Self-Organizing Maps
Applying Statistical Models and
Parametric Distance Measures for
Music Similarity Search
Fricke, Jobst P. A statistical theory of musical
consonance proved in praxis
Session Marketing and
Management Science II
11:20-
11:45
11:45-
12:10
Gazda, Vladimir On a Location of the Retail Units
and Equilibrium Price
Determination
Zeileis, Achim; Kleiber,
Christian
134
34
66
96
89
42
(Chair: Decker) Room
4
Recursive Partitioning of Economic
Regressions: Trees of Costly
Journals and Beautiful Professors
− xx −
50
168

12:10-
12:35
13:00-
14:00
van de Velden, Michel;
de Beuckelaer, Alain;
Groenen, Patrick;
Busing, Frank
Lunch (and Meetings)
Visualizing preferences using
minimum variance nonmetric
unfolding
Session Linguistics (Chairs: Goebl, Grzybek) Room
3
14:00-
14:25
14:25-
14:50
14:50-
15:15
15:15-
15:40
Rapp, Reinhard; Zock,
Michael
Fenk-Oczlon, Gertraud;
Fenk, August
Automatic Dictionary Expansion
Using Non-parallel Corpora
Cross-linguistic regularities in the
monosyllabic system
Rolshoven, Jürgen Grundzüge einer generativen
Korpuslinguistik
Petersen, Wiebke Lineare Kodierung multipler
Vererbungshierarchien:
Wiederbelebung einer antiken
Klassifikationsmethode
Session Invited Session: BCS (Chairs: Hennig, Murtagh) Room
2
14:00-
14:25
14:25-
14:50
Dean, Nema; Nugent,
Rebecca
Mirkin, Boris
Augmenting Model-Based
Clustering with Generalized
Linkage methods
Deviant box and dual clusters for the
analysis of conceptual contexts
14:50- Critchley, Frank; Pires, Principal Axis Analysis – with
15:15 Ana; Amado, Conceicao HDLSS bonuses!
15:15- Hennig, Christian; Using cluster analysis for species
15:40 Hausdorf, Bernhard delimitation
Session Processes in Industry (Chair: Joos) Room
6
14:00-
14:25
14:25-
14:50
14:50-
15:15
15:15-
15:40
Hahlweg, Cornelius;
Rothe, Hendrik
Raabe, Nils; Enk, Dirk;
Weihs, Claus;
Biermann, Dirk
Meier, René; Joos,
Franz
Große, Lars; Joos,
Franz
Auswertung hochaufgelöster
Streulichtdaten mit Methoden der
multivariaten Statistik
Dynamic disturbances in BTA
deephole drilling - Identification of
spiralling as a regenerative effect
Optimization Methods with
Evolutionary Algorithms and
Artificial Neural Networks
Usage of Artifical Neural Networks
for Data Handling
− xxi −
156
119
40
122
110
31
97
30
62
59
117
95
55

Session Marketing and Management
Science III
14:00-
14:25
14:25-
14:50
14:50-
15:15
15:15-
15:40
15:40-
15:55
15:55-
16:35
Lübke, Karsten;
Papenhoff, Heike
Wilczynski, Petra;
Sarstedt, Marko
(Chair: van den Poel) Room
4
Latent growth models for
analyzing a multi partner reward
program
88
Multi-Item Versus Single-Item
Measures: A Review and Future
Research Directions
161
Sommerfeld, Angela Trust as a Key Determinant of
Loyalty and its Moderators
Kneib, Thomas;
Baumgartner,
Bernhard; Steiner,
Winfried J.
Coffee
Semi-plenary Lectures
Celeux, Gilles Paul
et al.
Krolak-Schwerdt,
Sabine
Session Clustering and
Classification III
16:40- Müller-Funk, Ulrich;
17:05 Dlugosz, Stephan
17:05-
17:30
17:30-
17:55
Azam, Muhammad;
Ostermann, Alexander;
Pfeiffer, Karl-Peter
Gantner, Zeno;
Schmidt-Thieme, Lars
Session Optimization in
Statistics
16:40-
17:05
17:05-
17:30
Time-Varying Parameters in Brand
Choice Models
Choosing the number of clusters
in the latent class model (Chair:
Bock)
Strategies of model construction
for the analysis of judgement data
(Chair: Decker)
139
80
15 Room
5
82 Room
3
(Chair: Geyer-Schulz) Room
3
Predictive classification trees 99
Evaluation Criteria for the
Construction of Binary
Classification Trees with Two or
More Classes
Scalable and Incrementally
Updated Hybrid Recommender
Systems
Hansohm, Jürgen Algorithms for Computing the
Multivariate Isotonic Regression
Schachtner, Reinhard;
Pöppel, Gerhard; Lang,
Elmar
Nonnegative Matrix Factorization
for Binary Data to Extract
Elementary Failure Maps from
Wafer Test Images
− xxii −
7
47
(Chair: Ritter) Room
405/6
60
126

17:30-
17:55
Nalbantov, Georgi
Ilkov; Groenen, Patrick
J.F.; Bioch, Cor
Session Computational
Intelligence and
Metaheuristics
16:40-
17:05
17:05-
17:30
17:30-
17:55
Winkler, Stephan;
Affenzeller, Michael;
Wagner, Stefan;
Kronberger, Gabriel
Caserta, Marco;
Lessmann, Stefan
Support Vector Machines in the
Dual using Majorization and
Kernels
On the Effects of Enhanced
Selection Models on Quality and
Comparability of Classifiers
Produced by Genetic Programming
A novel approach to construct
discrete support vector machine
classifiers
Thorleuchter, Dirk Mining technologies in security
and defense
Session Miscellaneous Models
(Archeology)
16:40-
17:05
17:05-
17:30
Okada, Akinori;
Sakaehara, Towao
Gans, Ulrich-Walter;
Lang, Matthias
Session Education and
Psychology
16:40-
17:05
17:05-
17:30
17:30-
17:55
Fuchs, Sebastian;
Sarstedt, Marko
Strobl, Carolin; Leisch,
Friedrich
101
(Chair: Fink) Room
101/3
164
25
148
(Chair: Posluschny) Room
2
Analysis of Borrowing and
Guaranteeing Relationhships
among Government Officials at
the Eighth Century in the Old
Capital of Japan
106
ArcheoInf - Leistungszentrum für
die digitale Unterstützung
feldarchäologischer Projekte
46
(Chair: Krolak-Schwerdt) Room
6
On the Use of Student Samples in
Major Marketing Research
Journals. A Meta-Study
44
Who's Afraid of Statistics? -
Measurement and Predictors of
Statistics Anxiety in German
University Students
142
Ünlü, Ali Mosaic Plots and Knowledge
Structures
Session Marketing and Management
Science IV
17:05-
17:30
17:30-
17:55
Lam, Kar Yin; Koning,
Alex J.; Franses, Philip
Hans
Wagner, Ralf; Klaus,
Martin
155
(Chair: Decker) Room
4
Testing preference rankings 84
Exploring the Interaction
Structure of Weblogs
− xxiii −
91

18:00-
19:00
General Assembly of
GfKl
20:00 Conference Dinner (Handwerkskammer, Holsten-
wall 12, bus transfer 19:30)
09:00-
09:40
12:00-
12:20
Friday July 18, 2008
Semi-plenary Lectures
Palumbo, Francesco Clustering and Dimensionality
Reduction to Discover Interesting
Patterns in Binary Data (Chair:
Ultsch)
Wildner, Raimund Management and Methods: How
to do Market Segmentation
Projects (Chair: Fantapié
Altobelli)
Coffee
Session Clustering and
Classification IV
10:00-
10:25
10:25-
10:50
10:50-
11:15
Buza, Krisztian Antal;
Schmidt-Thieme, Lars
Room
5
109 Room
5
162 Room
3
(Chair: Groenen) Room
3
Motif-based Classification of Time
Series with Bayesian Networks
and SVMs
23
Tomas, Amber Issues related to the
implementation of a dynamic
logistic model for classifier
combination
Oosthuizen, Surette;
Steel, Sarel J.
Session Visualization and
Scaling Methods I
10:00-
10:25
10:25-
10:50
10:50-
11:15
Variable selection for kernel
classifiers: a feature-to-input
space approach
Mucha, Hans-Joachim Clustering a Contingency Table
Accompanied by Visualization
Bocci, Laura; Vichi,
Maurizio
The K-INDSCAL Model for
Heterogeneous Three-way
Dissimilarity Data
150
107
(Chair: Hennig) Room
405/6
Cortina-Borja, Mario Extending Multivariate Planing 29
− xxiv −
98
17

Session Exploratory Data
Analysis I
10:00-
10:25
10:25-
10:50
10:50-
11:15
Chiou, Hua-Kai; Yuan,
Benjamin J.C.; Wang,
Yen-Wen
Cernian, Alexandra;
Carstoiu, Dorin;
Ionescu, Tudor
Einbeck, Jochen;
Evers, Ludger
(Chair: Wehrens) Room
101/3
Correspondence Analysis for
Exploring the Implementation of
One Village One Product Programs
in Taiwan
28
Modeling the Classification of
Heterogeneous Data
26
Data compression and regression
based on local principal curves
Session Spatial Planning I (Chair: Behnisch) Room
2
10:00- Behnisch, Martin; Estimating the number of
11
10:25 Ultsch, Alfred
buildings in Germany
10:25-
10:50
10:50-
11:15
Thiel, Klaus Optimal VDSL Expansion taking
into Consideration of
Infrastructure Restrictions and
Marketing Requirements
Aden, Christian;
Mucha, Hans-Joachim;
Schmidt, Gunther;
Schröder, Winfried
Session Medical and Health
Sciences I
10:00-
10:25
10:25-
10:50
10:50-
11:15
Augustin, Thomas;
Wallner, Matthias
WaldIS - a web based reference
system for the forest monitoring
in North Rhine-Westphalia
36
145
(Chair: Lausen) Room
6
On the power of corrected score
functions to adjust for
measurement error
6
Sieben, Wiebke Time Related Features for Alarm
Classification in Intensive Care
Monitoring
Ostermann, Thomas;
Schuster, Reinhard;
Erben, Christoph
Session Market Research,
Controlling, OR I
10:00-
10:25
10:25-
10:50
Brusch, Michael; Baier,
Daniel
Classifying hospitals with respect
to their diagnostic diversity using
Shannon's entropy
3
135
108
(Chair: Baier) Room
4
Analyzing the Stability of Price
Response Functions - Measuring
the Influence of Different
Parameters in a Monte Carlo
Comparison
22
Tarka, Piotr Conjoint Analysis within the field
of customer satisfaction problems
– a model of composite
product/service
− xxv −
144

10:50-
11:15
11:20-
12:00
12:00-
12:20
Punzo, Antonio Considerations on the impact of
JML-ill-conditioned configurations
in the CML approach
Semi-plenary Lectures
Ben-Israel, Adi Probabilistic Distance Clustering
(Chair: Vichi)
Imaizumi, Tadashi Dimensionality Reduction of
Similarity Matrix (Chair: Gaul)
Coffee
Session Clustering and
Classification V
12:20-
12:45
12:45-
13:30
Kludas, Jana; Bruno,
Eric; Marchand-Maillet,
Stepahne
Schiffner, Julia;
Szepannek, Gero;
Monthé, Thierry;
Weihs, Claus
Session Visualization and
Scaling Methods II
12:20-
12:45
12:45-
13:30
116
12 Room
5
73 Room
3
(Chair: Godehart) Room
3
Exploiting synergetic and
redundant features for multimedia
document classification
79
Localized Logistic Regression for
Discrete Influential Factors
129
(Chair: van de Felden) Room
405/6
Adachi, Kohei Joint Procrustes Analysis with
Constrained Simplimax Rotation:
Nonsingular Transformation of
Component Score and Loading
Matrices Toward Simple Structure
Fernández-Aguirre,
Karmele; Garín-Martín,
María Araceli
Session Exploratory Data
Analysis II
12:20- Zarraga, Amaya;
12:45 Goitisolo, Beatriz
12:45-
13:30
Nusser, Sebastian;
Otte, Clemens;
Hauptmann, Werner
Validity of images from binary
coding tables. Student motivation
surveys: some evidence
(Chair: Wehrens) Room
101/3
Factor Analysis of Incomplete
Disjunctive Tables
167
Multi-Class Extension of Verifiable
Ensemble Models for Safety-
Related Applications
105
− xxvi −
2
41

Session Spatial Planning II (Chair: Behnisch) Room
2
12:20-
12:45
12:45-
13:30
Witek, Ewa Analysis of massive emigration
from Poland - the model-based
clustering approach
Thinh, Nguyen Xuan;
Küttner, Leander;
Meinel, Gotthard
Session Medical and Health
Sciences II
12:20-
12:45
12:45-
13:30
Henker, Uwe; Ultsch,
Alfred; Petersohn, Uwe
Schuster, Reinhard;
von Arnstedt, Eva
Session Market Research,
Controlling, OR II
12:20-
12:45
12:45-
13:30
13:15-
14:00
14:00-
15:00
Esber, Said; Baier,
Daniel
Abu Assab, Samah;
Baier, Daniel
Evaluate the data structure and
identify homogenous spatial units
in the data base "Sustainability
issues in sensitive areas" of the
EU-FP6 Integrated Project
SENSOR
165
146
(Chair: Lausen) Room
6
Die präzise und effizienzte
Erkennung von medizinischen
Anforderungsformularen
61
Age Distributions for costs in drug
prescription by practitioners and
for DRG-based hospital treatment
133
(Chair: Baier) Room
4
Realoptionen bei der Bewertung
von neuen Produkten
38
Designing Products Using Quality
Function Deployment and Conjoint
Analysis: A Comparison in a
Market for Elderly People
Plenary Lecture (Chair: Weihs) Room
5
McMorris, Fred R. Majority-rule consensus: from
preferences (social choice) to
trees (biology and classification
theory)
94
Informal Farewell (Conference site)
− xxvii −
1

List of Contributions
Authors Title Page
Abu Assab, Samah; Baier, Designing Products Using Quality Function 1
Daniel
Deployment and Conjoint Analysis: A
Comparison in a Market for Elderly People
Adachi, Kohei Joint Procrustes Analysis with Constrained
Simplimax Rotation: Nonsingular
Transformation of Component Score and
Loading Matrices Toward Simple Structure
2
Aden, Christian; Mucha, Hans- WaldIS - a web based reference system 3
Joachim; Schmidt, Gunther; for the forest monitoring in North Rhine-
Schröder, Winfried
Westphalia
Adler, Werner; Brenning, Classification of Paired Data Using
4
Alexander; Lausen, Berthold Ensemble Methods
Andres, Bjoern; Koethe, Ullrich;
Helmstaedter, Moritz; Denk,
Winfried; Hamprecht, Fred
Segmentation of Neural Tissue 5
Augustin, Thomas; Wallner, On the power of corrected score functions 6
Matthias
to adjust for measurement error
Azam, Muhammad; Ostermann, Evaluation Criteria for the Construction of 7
Alexander; Pfeiffer, Karl-Peter Binary Classification Trees with Two or
More Classes
Bade, Korinna; Benz, Dominik Evaluation Strategies for Learning
Algorithms of Hierarchical Structures
8
Barbosa, Rui Pedro; Belo,
Orlando
Autonomous Forex Trading Agents 9
Becker, Niels; Werners, Brigitte Improving Product Line Design with
Bundling
10
Behnisch, Martin; Ultsch, Alfred Estimating the number of buildings in
Germany
11
Ben-Israel, Adi Probabilistic Distance Clustering 12
Bessler, Wolfgang; Holler, Hedge Funds in a Bayesian Asset
13
Julian
Allocation Framework: Incorporating
Information on market states and
manager's ability
Betzin, Jörg Categorical Data in PLS Path modeling 14
Biernacki, Christophe; Celeux, Choosing the number of clusters in the 15
Gilles Paul; Govaert, Gérard latent class model
Bisson, Gilles Clustering of molecules and structured
data
16
Bocci, Laura; Vichi, Maurizio The K-INDSCAL Model for Heterogeneous
Three-way Dissimilarity Data
17
Borgelt, Christian Weighting and Selecting Features in Fuzzy
Clustering
18
Boulesteix, Anne-Laure; On optimistic bias in reporting microarray- 20
Slawski, Martin
based classification accuracy
− xxviii −

Bravo, Cristian; Maldonado, Practical experiences from Credit Scoring 21
Sebastian; Weber, Richard projects for Chilean financial organizations
Brusch, Michael; Baier, Daniel Analyzing the Stability of Price Response
Functions - Measuring the Influence of
Different Parameters in a Monte Carlo
Comparison
22
Buza, Krisztian Antal; Schmidt- Motif-based Classification of Time Series 23
Thieme, Lars
with Bayesian Networks and SVMs
Calò, Daniela G.; Viroli, Cinzia Visualizing data in Gaussian mixture
model classification
24
Caserta, Marco; Lessmann, A novel approach to construct discrete 25
Stefan
support vector machine classifiers
Cernian, Alexandra; Carstoiu, Modeling the Classification of
26
Dorin; Ionescu, Tudor
Heterogeneous Data
Chiou, Hua-Kai; Huang, Yong- Applying Rough Set Theory to
27
Ting; Liu, Gia-Shie
Constructing Knowledge Base for Critical
Military Commodity Management
Chiou, Hua-Kai; Yuan,
Correspondence Analysis for Exploring the 28
Benjamin J.C.; Wang, Yen-Wen Implementation of One Village One
Product Programs in Taiwan
Cortina-Borja, Mario Extending Multivariate Planing 29
Critchley, Frank; Pires, Ana; Principal Axis Analysis – with HDLSS 30
Amado, Conceicao
bonuses!
Dean, Nema; Nugent, Rebecca Augmenting Model-Based Clustering with
Generalized Linkage methods
31
Desmet, Frank Michel; Leman, Statistical analysis of human body
32
Marc; Lesaffre, Micheline movement and group interactions in
response to music
Dias, José G.; Vermunt, Jeroen Mixture Hidden Markov Models in Finance 33
K.; Ramos, Sofia
Research
Dolata, Jens; Mucha, Hans- Mapping Findspots of Roman Military 34
Joachim; Bartel, Hans-Georg Brickstamps in Mogontiacum (Mainz) and
Archaeometrical Analysis
Eigenfeldt, Arne; Kapur, Ajay Multimodal Performance Analysis of
Electronic Sitar
35
Einbeck, Jochen; Evers, Ludger Data compression and regression based
on local principal curves
36
Enyukov, Igor Regression-autoregression based
clustering
37
Esber, Said; Baier, Daniel Realoptionen bei der Bewertung von
neuen Produkten
38
Fenk-Oczlon, Gertraud; Fenk, Cross-linguistic regularities in the
40
August
monosyllabic system
Fernández-Aguirre, Karmele; Validity of images from binary coding 41
Garín-Martín, María Araceli tables. Student motivation surveys: some
evidence
Fricke, Jobst P. A statistical theory of musical consonance
proved in praxis
42
− xxix −

Fritsch, Arno; Ickstadt, Katja An Improved Criterion for Clustering
Based on the Posterior Similarity Matrix
Fuchs, Sebastian; Sarstedt, On the Use of Student Samples in Major
Marko
Marketing Research Journals. A Meta-
Study
Gabriel, Thomas R.; Thiel, Multi-Dimensional Scaling applied to
Kilian; Berthold, Michael R. Hierarchical Fuzzy Rule Systems
Gans, Ulrich-Walter; Lang, ArcheoInf - Leistungszentrum für die
Matthias
digitale Unterstützung feldarchäologischer
Projekte
Gantner, Zeno; Schmidt- Scalable and Incrementally Updated
Thieme, Lars
Hybrid Recommender Systems
Garel, Bernard; Boucharel, Non-Gaussian nature of ENSO signals and
Julien; Dewitte, Boris; du climate shifts: implications for regional
Penhoat, Yves
studies off the western coast of South
America
Gassiat, Elisabeth Likelihood ratio test for general mixture
models
Gazda, Vladimir On a Location of the Retail Units and
Equilibrium Price Determination
Geyer-Schulz, Andreas; Hoser, The Potential of Social Intelligence for
Bettina
Collective Intelligence
Godehardt, Erhard; Jaworski, Isolated vertices in random intersection
Jerzy; Rybarczyk, Katarzyna graphs
Greselin, Francesca; Ingrassia, A note on constrained EM algorithms for
Salvatore
mixtures of elliptical distributions
Groenen, Patrick J.F.;
Support Vector Machines in the Primal
Nalbantov, Georgi; Bioch, Cor using Majorization and Kernels
Große, Lars; Joos, Franz Usage of Artifical Neural Networks for
Data Handling
Grün, Bettina; Leisch, Friedrich Model diagnostics of finite mixtures using
bootstrapping
Haasdonk, Bernard; Pekalska, Classification with Regularized Kernel
Elzbieta
Mahalanobis-Distances
Häberle, Lothar On classification of species of
representation rings
Hahlweg, Cornelius; Rothe, Auswertung hochaufgelöster
Hendrik
Streulichtdaten mit Methoden der
multivariaten Statistik
Hansohm, Jürgen Algorithms for Computing the Multivariate
Isotonic Regression
Henker, Uwe; Ultsch, Alfred; Die präzise und effizienzte Erkennung von
Petersohn, Uwe
medizinischen Anforderungsformularen
Hennig, Christian; Hausdorf, Using cluster analysis for species
Bernhard
delimitation
Henseler, Jörg Nonlinear Effects in PLS Path Models: A
Comparison of Available Approaches
Hermes, Jürgen; Schwiebert, Classification of text processing
Stephan
components: The Tesla Role System
− xxx −
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64

Herrmann, Lutz; Ultsch, Alfred Strengths and Weaknesses of Ant Colony
Clustering
65
Herzog, Irmela Reconstructing Central Places and
Settlements Groups
66
Hielscher, Thomas; Zucknick, On the prognostic value of gene
67
Manuela; Werft, Wiebke;
Benner, Axel
expression signatures for censored data
Holzmann, Hajo; Dannemann, Likelihood ratio testing for hidden Markov 68
Jörn
models
Hühn, Jens; Hüllermeier, Eyke Rule-Based Learning of Reliable Classifiers 69
Huellermeier, Eyke;
Combining Predictions in Pairwise
70
Vanderlooy, Stijn
Classification: An Adaptive Voting
Strategy and Its Relation to Weighted
Voting
Huson, Daniel H.; Rupp, Regula Using Cluster Networks to Represent Non-
Compatible Sets of Clusters
71
Hütt, Marc-Thorsten Genome phylogeny based on short-range
correlations in DNA sequences
72
Imaizumi, Tadashi Dimensionality Reduction of Similarity
Matrix
73
Kaiser, Sebastian; Leisch,
Friedrich
Benchmarking Bicluster Algorithms 75
Karatzoglou, Alexandros; Nonparametric distribution analysis for 76
Feinerer, Ingo; Hornik, Kurt text mining
Klein, Christian; Kundisch, Index-Based Investment Vehicles - A 77
Dennis
Comparative Study for the German DAX
Klenk, Hans-Peter Polyphasic genomic approach for the
taxonomy of archaea and bacteria
78
Kludas, Jana; Bruno, Eric; Exploiting synergetic and redundant 79
Marchand-Maillet, Stepahne features for multimedia document
classification
Kneib, Thomas; Baumgartner, Time-Varying Parameters in Brand Choice 80
Bernhard; Steiner, Winfried J. Models
Koralun-Bereznicka, Julia Multivariate comparative analysis of stock
exchanges - the European perspective
81
Krolak-Schwerdt, Sabine Strategies of model construction for the
analysis of judgement data
82
Kuziak, Katarzyna An application of copula functions to
market risk management
83
Lam, Kar Yin; Koning, Alex J.;
Franses, Philip Hans
Testing preference rankings 84
Latouche, Pierre J.; Ambroise,
Christophe; Birmelé, Etienne
Bayesian Methods for Graph Clustering 85
Locarek-Junge, Hermann; Fundamental Indexation - testing the 86
Mihm, Max
concept in the German stock market
Louw, Nelmarie; Lamont, Identifying Atypical Cases in Kernel Fisher 87
Morne; Steel, Sarel
Discriminant Analysis by using the
Smallest Enclosing Hypersphere
− xxxi −

Lübke, Karsten; Papenhoff, Latent growth models for analyzing a 88
Heike
multi partner reward program
Lukashevich, Hanna; Dittmar, Applying Statistical Models and Parametric 89
Christian; Bastuck, Christoph Distance Measures for Music Similarity
Search
Lukociene, Olga; Vermunt, Determining the number of components in 90
Jeroen K.
mixture models for hierarchical data
Klaus, Martin; Wagner, Ralf Exploring the Interaction Structure of
Weblogs
91
Martin-Magniette, Marie-Laure; ChIPmix : Mixture model of regressions 92
Mary-Huard, Tristan; Bérard,
Caroline; Robin, Stéphane
for ChIP-chip experiment analysis
McLachlan, Geoffrey John Clustering of High-Dimensional Data Via
Finite Mixture Models
93
McMorris, F. R. Majority-rule consensus: from preferences
(social choice) to trees (biology and
classification theory)
94
Meier, René; Joos, Franz Optimization Methods with Evolutionary
Algorithms and Artificial Neural Networks
95
Meyer, Florian; Ultsch, Alfred Finding Music Fads by clustering Online
Radio Data with Emergent Self-Organizing
Maps
96
Mirkin, Boris Deviant box and dual clusters for the
analysis of conceptual contexts
97
Mucha, Hans-Joachim Clustering a Contingency Table
Accompanied by Visualization
98
Müller-Funk, Ulrich; Dlugosz,
Stephan
Predictive classification trees 99
Mylonas, Phivos; Solachidis, Efficient Media Exploitation towards 100
Vassilios; Geyer-Schulz,
Andreas; Hoser, Bettina;
Chapman, Sam; Ciravegna,
Fabio; Staab, Stefen; Smrz,
Pavel; Kompatsiaris, Yiannis;
Avrithis, Yannis
Collective Intelligence
Nalbantov, Georgi Ilkov; Support Vector Machines in the Dual using 101
Groenen, Patrick J.F.; Bioch,
Cor
Majorization and Kernels
Neumann, Anneke; Ambrosi, Approach for Dynamic Problems in
102
Klaus; Hahne, Felix
Clustering
Neykov, Neyko; Filzmoser, Robust fitting of mixtures: The approach 103
Peter; Neytchev, Plamen based on the Trimmed Likelihood
Estimator
Nugent, Rebecca; Stuetzle, Cluster Tree Estimation using a
104
Werner
Generalized Single Linkage Method
Nusser, Sebastian; Otte, Multi-Class Extension of Verifiable Ensem- 105
Clemens; Hauptmann, Werner ble Models for Safety-Related Applications
Okada, Akinori; Sakaehara, Analysis of Borrowing and Guaranteeing 106
Towao
Relationhships among Government
Officials at the Eighth Century in the Old
− xxxii −

Capital of Japan
Oosthuizen, Surette; Steel, Variable selection for kernel classifiers: a 107
Sarel J.
feature-to-input space approach
Ostermann, Thomas; Schuster, Classifying hospitals with respect to their 108
Reinhard; Erben, Christoph diagnostic diversity using Shannon's
entropy
Palumbo, Francesco Clustering and Dimensionality Reduction
to Discover Interesting Patterns in Binary
Data
109
Petersen, Wiebke Lineare Kodierung multipler
Vererbungshierarchien: Wiederbelebung
einer antiken Klassifikationsmethode
110
Petersen, Wiebke; Heinrich, Begriffsanalytischer Ansatz zur
111
Petja
qualitativen Zitationsanalyse
Piontek, Krzysztof The Analysis of the power for some
chosen VaR backtesting procedures -
simulation approach
112
Pommeret, Denys Testing distribution in errors in variables
models
113
Pons, Odile Classification with an increasing number of
components
114
Potapov, Sergej; Lausen,
Berthold
Bagging with different split criteria 115
Punzo, Antonio Considerations on the impact of JML-illconditioned
configurations in the CML
approach
116
Raabe, Nils; Enk, Dirk; Weihs, Dynamic disturbances in BTA deephole 117
Claus; Biermann, Dirk
drilling - Identification of spiralling as a
regenerative effect
Radermacher, Walter Statistical processes under change -
Enhancing data quality with pretests
118
Rapp, Reinhard; Zock, Michael Automatic Dictionary Expansion Using
Non-parallel Corpora
119
Ringle, Christian M. FIMIX-PLS Segmentation of Data for Path
Models with Multiple Endogenous LVs
120
Rokita, Pawel; Piontek,
Extreme unconditional dependence vs. 121
Krzysztof
multivariate GARCH effect in the analysis
of dependence between high losses on
Polish and German stock indexes
Rolshoven, Jürgen Grundzüge einer generativen
Korpuslinguistik
122
Rozmus, Dorota Cluster ensemble based on co-occurrence
data
123
Sagan, Adam; Kowalska- Dyadic Interactions in Service Encounter - 124
Musial, Magdalena
Bayesian SEM Approach
− xxxiii −

Sardet, Laure; Patilea, Valentin Beta-kernel density estimation using
mixture-based transformations: an
application to claims distribution
Schachtner, Reinhard; Pöppel,
Gerhard; Lang, Elmar
Scharl, Theresa; Leisch,
Friedrich
Nonnegative Matrix Factorization for
Binary Data to Extract Elementary Failure
Maps from Wafer Test Images
Quality-Based Clustering of Functional
Data: Applications to Time Course
Microarray Data
Multilingual knowledge based concept
recognition in textual data
Localized Logistic Regression for Discrete
Influential Factors
Schierle, Martin; Trabold,
Daniel
128
Schiffner, Julia; Szepannek,
Gero; Monthé, Thierry; Weihs,
Claus
129
Schiffner, Julia; Weihs, Claus Localized Classification Using Mixture
Models
130
Schlattmann, Peter Comparison of four estimators of the
heterogeneity variance for meta-analysis
131
Schölkopf, Bernhard Machine Learning applications of positive
definite kernels
132
Schuster, Reinhard; von Age Distributions for costs in drug
133
Arnstedt, Eva
prescription by practitioners and for DRGbased
hospital treatment
Schyle, Daniel The Late Neolithic flint axe production on
the Lousberg (Aachen, Germany) – An
extrapolation of supply and demand and
population density
134
Sieben, Wiebke Time Related Features for Alarm
Classification in Intensive Care Monitoring
135
Slawski, Martin; Boulesteix, 'CMA' - Steps in developing a
136
Anne-Laure; Daumer, Martin comprehensive R-toolbox for classification
with microarray data and other highdimensional
problems
Solachidis, Vassilios; Mylonas,
Phivos; Geyer-Schulz, Andreas;
Hoser, Bettina; Chapman, Sam;
Ciravegna, Fabio; Staab,
Stefen; Contopoulos, Costis;
Gkika, Ioanna; Smrz, Pavel;
Kompatsiaris, Yiannis; Avrithis,
Yannis
Generating Collective Intelligence 137
Sommer, Katrin; Weihs, Claus Analysis of polyphonic musical time series 138
Sommerfeld, Angela Trust as a Key Determinant of Loyalty and
its Moderators
139
Stecking, Ralf; Schebesch, Generating Fictitious Training Data for 140
Klaus B.
Credit Client Classification
Steinbrecher, Matthias; Kruse, Clustering Association Rules with Fuzzy 141
Rudolf
Concepts
− xxxiv −
125
126
127

Strobl, Carolin; Leisch,
Friedrich
Who's Afraid of Statistics? - Measurement
and Predictors of Statistics Anxiety in
German University Students
Strobl, Carolin; Zeileis, Achim A New, Conditional Variable Importance
Measure for Random Forests
143
Tarka, Piotr Conjoint Analysis within the field of
customer satisfaction problems – a model
of composite product/service
144
Thiel, Klaus Optimal VDSL Expansion taking into
Consideration of Infrastructure
Restrictions and Marketing Requirements
145
Thinh, Nguyen Xuan; Küttner, Evaluate the data structure and identify 146
Leander; Meinel, Gotthard homogenous spatial units in the data base
"Sustainability issues in sensitive areas" of
the EU-FP6 Integrated Project SENSOR
Thorleuchter, Dirk Mining ideas from textual information 147
Thorleuchter, Dirk Mining technologies in security and
defense
148
Timmerman, Marieke E.; Multilevel Simultaneous Component 149
Lichtwarck-Aschoff, Anna; Analysis for Studying Inter-individual and
Ceulemans, Eva
Intra-individual Variabilities
Tomas, Amber Issues related to the implementation of a
dynamic logistic model for classifier
combination
150
Trinchera, Laura; Esposito A Comprehensive Partial Least Squares 151
Vinzi, Vincenzo
Approach to Component-Based Structural
Equation Modeling
Trzesiok, Michal Relevant Importance of Predictor Variables
in Support Vector Machines Models
152
Ultsch, Alfred Comparison of Algorithms to find
differentially expressed Genes in
Microarray Data
153
Ultsch, Alfred Is log ratio a good value for measuring
return in stock investments?
154
Ünlü, Ali Mosaic Plots and Knowledge Structures 155
van de Velden, Michel; de Visualizing preferences using minimum 156
Beuckelaer, Alain; Groenen,
Patrick; Busing, Frank
variance nonmetric unfolding
van der Ark, Andries L.; Straat, Selection of items for tests and
157
J. Hendrik
questionnaires using Mokken scale
analysis
van der Heijden, Peter G.M. Estimating the prevalence of rule
transgression
158
Wagner, Ralf; Sauerwald, Erik Clustering Consumers with Respect to
Their Marketing Reactance Behavior
159
Wehrens, Ron Supervised Self-Organising Maps and
More
160
Wilczynski, Petra; Sarstedt, Multi-Item Versus Single-Item Measures: 161
Marko
A Review and Future Research Directions
− xxxv −
142

Wildner, Raimund Management and methods: How to do
market segmentation projects
162
Winkler, Roland; Rehm, Frank;
Kruse, Rudolf
Clustering with Repulsive Prototypes 163
Winkler, Stephan; Affenzeller, On the Effects of Enhanced Selection 164
Michael; Wagner, Stefan; Models on Quality and Comparability of
Kronberger, Gabriel
Classifiers Produced by Genetic
Programming
Witek, Ewa Analysis of massive emigration from
Poland - the model-based clustering
approach
165
Worm, Katja; Meffert, Beate Image Based Mail Piece Identification
using Unsupervised Learning
166
Zarraga, Amaya; Goitisolo, Factor Analysis of Incomplete Disjunctive 167
Beatriz
Tables
Zeileis, Achim; Kleiber,
Recursive Partitioning of Economic
168
Christian
Regressions: Trees of Costly Journals and
Beautiful Professors
− xxxvi −

Author Index
Abu Assab, Samah 1
Adachi, Kohei 2
Aden, Christian 3
Adler, Werner 4
Affenzeller, Michael 164
Amado, Conceicao 30
Ambroise, Christophe 85
Ambrosi, Klaus 102
Andres, Bjoern 5
Augustin, Thomas 6
Avrithis, Yannis 100, 137
Azam, Muhammad 7
Bade, Korinna 8
Baier, Daniel 1, 22, 38
Barbosa, Rui Pedro 9
Bartel, Hans-Georg 34
Bastuck, Christoph 89
Baumgartner, Bernhard 80
Becker, Niels 10
Behnisch, Martin 11
Belo, Orlando 9
Ben-Israel, Adi 12
Benner, Axel 67
Benz, Dominik 8
Bérard, Caroline 92
Berthold, Michael R. 45
Bessler, Wolfgang 13
Betzin, Jörg 14
Biermann, Dirk 117
Biernacki, Christophe 15
Bioch, Cor 54, 101
Birmelé, Etienne 85
Bisson, Gilles 16
Bocci, Laura 17
Borgelt, Christian 18
Boucharel, Julien 48
Boulesteix, Anne-Laure 20, 136
− xxxvii −
Bravo, Cristian 21
Brenning, Alexander 4
Bruno, Eric 79
Brusch, Michael 22
Busing, Frank 156
Buza, Krisztian Antal 23
Calò, Daniela G. 24
Carstoiu, Dorin 26
Caserta, Marco 25
Celeux, Gilles Paul 15
Cernian, Alexandra 26
Ceulemans, Eva 149
Chiou, Hua-Kai 27, 28
Ciravegna, Fabio 100, 137
Contopoulos, Costis 137
Cortina-Borja, Mario 29
Critchley, Frank 30
Dannemann, Jörn 68
Daumer, Martin 136
de Beuckelaer, Alain 156
Dean, Nema 31
Denk, Winfried 5
Desmet, Frank Michel 32
Dewitte, Boris 48
Dias, José G. 33
Dittmar, Christian 89
Dlugosz, Stephan 99
Dolata, Jens 34
du Penhoat, Yves 48
Eigenfeldt, Arne 35
Einbeck, Jochen 36
Enk, Dirk 117
Enyukov, Igor 37
Erben, Christoph 108
Esber, Said 38
Esposito Vinzi, Vincenzo 151
Evers, Ludger 36

Feinerer, Ingo 76
Fenk, August 40
Fenk-Oczlon, Gertraud 40
Fernández-Aguirre,
Karmele
41
Filzmoser, Peter 103
Franses, Philip Hans 84
Fricke, Jobst P. 42
Fritsch, Arno 43
Fuchs, Sebastian 44
Gabriel, Thomas R. 45
Gans, Ulrich-Walter 46
Gantner, Zeno 47
Garel, Bernard 48
Garín-Martín, María Araceli 41
Gassiat, Elisabeth 49
Gazda, Vladimir 50
Geyer-Schulz, Andreas 51, 100,
137
Gkika, Ioanna 137
Godehardt, Erhard 52
Goitisolo, Beatriz 167
Govaert, Gérard 15
Greselin, Francesca 53
Groenen, Patrick J.F. 54, 101,
156
Große, Lars 55
Grün, Bettina 56
Haasdonk, Bernard 57
Häberle, Lothar 58
Hahlweg, Cornelius 59
Hahne, Felix 102
Hamprecht, Fred A. 5
Hansohm, Jürgen 60
Hauptmann, Werner 105
Hausdorf, Bernhard 62
Heinrich, Petja 111
Helmstaedter, Moritz 5
Henker, Uwe 61
Hennig, Christian 62
Henseler, Jörg 63
− xxxviii −
Hermes, Jürgen 64
Herrmann, Lutz 65
Herzog, Irmela 66
Hielscher, Thomas 67
Holler, Julian 13
Holzmann, Hajo 68
Hornik, Kurt 76
Hoser, Bettina 51, 100,
137
Huang, Yong-Ting 27
Hühn, Jens 69
Hüllermeier, Eyke 69, 70
Hütt, Marc-Thorsten 72
Huson, Daniel H. 71
Ickstadt, Katja 43
Imaizumi, Tadashi 73
Ingrassia, Salvatore 53
Ionescu, Tudor 26
Jaworski, Jerzy 52
Joos, Franz 55, 95
Kapur, Ajay 35
Karatzoglou, Alexandros 76
Klaus, Martin 91
Kleiber, Christian 168
Klein, Christian 77
Klenk, Hans-Peter 78
Kludas, Jana 79
Kneib, Thomas 80
Koethe, Ullrich 5
Kompatsiaris, Yiannis 100, 137
Koning, Alex J. 84
Koralun-Bereznicka, Julia 81
Kowalska-Musial, Magdal. 124
Krolak-Schwerdt, Sabine 82
Kronberger, Gabriel 164
Kruse, Rudolf 141, 163
Küttner, Leander 146
Kundisch, Dennis 77
Kuziak, Katarzyna 83
Lam, Kar Yin 84

Lamont, Morne 87
Lang, Elmar 126
Lang, Matthias 46
Latouche, Pierre J. 85
Lausen, Berthold 4, 115
Leisch, Friedrich 56, 75,
127, 142
Leman, Marc 32
Lesaffre, Micheline 32
Lessmann, Stefan 25
Lichtwarck-Aschoff, Anna 149
Locarek-Junge, Hermann 86
Louw, Nelmarie 87
Lübke, Karsten 88
Lukashevich, Hanna 89
Lukociene, Olga 90
Maldonado, Sebastian 21
Marchand-Maillet, Stepahne 79
Martin-Magniette, Marie-L. 92
Mary-Huard, Tristan 92
McLachlan, Geoffrey John 93
McMorris, Fred R. 94
Meffert, Beate 166
Meier, René 95
Meinel, Gotthard 146
Mihm, Max 86
Mirkin, Boris 97
Monthé, Thierry 129
Mucha, Hans-Joachim 3, 34, 98
Müller-Funk, Ulrich 99
Mylonas, Phivos 100, 137
Nalbantov, Georgi Ilkov 54, 101
Neumann, Anneke 102
Neykov, Neyko 103
Neytchev, Plamen 103
Nugent, Rebecca 31, 104
Nusser, Sebastian 105
Okada, Akinori 106
Oosthuizen, Surette 107
Ostermann, Alexander 7
− xxxix −
Ostermann, Thomas 108
Otte, Clemens 105
Palumbo, Francesco 109
Papenhoff, Heike 88
Patilea, Valentin 125
Pekalska, Elzbieta 57
Petersen, Wiebke 110, 111
Petersohn, Uwe 61
Pfeiffer, Karl-Peter 7
Piontek, Krzysztof 112, 121
Pires, Ana 30
Pöppel, Gerhard 126
Pommeret, Denys 113
Pons, Odile 114
Potapov, Sergej 115
Punzo, Antonio 116
Raabe, Nils 117
Radermacher, Walter 118
Ramos, Sofia 33
Rapp, Reinhard 119
Rehm, Frank 163
Ringle, Christian M. 120
Robin, Stéphane 92
Rokita, Pawel 121
Rolshoven, Jürgen 122
Rothe, Hendrik 59
Rozmus, Dorota 123
Rupp, Regula 71
Rybarczyk, Katarzyna 52
Sagan, Adam 124
Sakaehara, Towao 106
Sardet, Laure 125
Sarstedt, Marko 44, 161
Sauerwald, Erik 159
Schachtner, Reinhard 126
Scharl, Theresa 127
Schebesch, Klaus B. 140
Schierle, Martin 128
Schiffner, Julia 129, 130
Schlattmann, Peter 131

Schmidt, Gunther 3
Schmidt-Thieme, Lars 23, 47
Schölkopf, Bernhard 132
Schröder, Winfried 3
Schuster, Reinhard 108, 133
Schwiebert, Stephan 64
Schyle, Daniel 134
Sieben, Wiebke 135
Slawski, Martin 20, 136
Smrz, Pavel 100, 137
Solachidis, Vassilios 100, 137
Sommer, Katrin 138
Sommerfeld, Angela 139
Staab, Stefen 100, 137
Stecking, Ralf 140
Steel, Sarel J. 87, 107
Steinbrecher, Matthias 141
Steiner, Winfried J. 80
Straat, J. Hendrik 157
Stuetzle, Werner 104
Szepannek, Gero 129
Tarka, Piotr 144
Thiel, Kilian 45
Thiel, Klaus 145
Thinh, Nguyen Xuan 146
Thorleuchter, Dirk 147, 148
Timmerman, Marieke E. 149
Tomas, Amber 150
Trabold, Daniel 128
Trinchera, Laura 151
Trzesiok, Michal 152
Ünlü, Ali 155
Ultsch, Alfred 11, 61,
65, 96,
153, 154
van de Velden, Michel 156
− xl −
van der Ark, Andries L. 157
van der Heijden, Peter G.M. 158
Vanderlooy, Stijn 70
Vermunt, Jeroen K. 33, 90
Vichi, Maurizio 17
Viroli, Cinzia 24
von Arnstedt, Eva 133
Wagner, Ralf 91, 159
Wagner, Stefan 164
Wallner, Matthias 6
Weber, Richard 21
Wehrens, Ron 160
Weihs, Claus 117,
129,
130, 138
Werft, Wiebke 67
Werners, Brigitte 10
Wilczynski, Petra 161
Wildner, Raimund 162
Winkler, Roland 163
Winkler, Stephan 164
Witek, Ewa 165
Worm, Katja 166
Yuan, Benjamin J.C. 28
Zarraga, Amaya 167
Zeileis, Achim 143, 168
Zock, Michael 119
Zucknick, Manuela 67

Designing Products Using Quality Function
Deployment and Conjoint Analysis: A
Comparison in a Market for Elderly People
Samah Abu Assab and Daniel Baier
Chair of Marketing and Innovation Management, Brandenburg University of
Technology, Erich-Weinert-Str. 1, 03046 Cottbus, Germany
samah.assab@tu-cottbus.de, baier@tu-cottbus.de
Abstract. In this paper, we compare two product design approaches namely; quality
function deployment (QFD) and conjoint analysis (CA) on the example of mobile
phones for elderly people as a target group. Then, we compare between our results
and the results from former similar comparisons (e.g., Pullman et al. (2002), Katz
(2004)). In this work, the same procedures and conditions are taken into consideration
as that taken by Pullman et al. in their paper (2002).
Pullman et al. (2002) view the relation between the two methods: QFD and
CA as a complementary one in which both should be simultaneously implemented
and each providing feedback to the other. They concluded that CA is more efficient
in reflecting the end-users’ present preferences for the product attributes, whereas
QFD is definitely better in satisfying end-users’ needs from the developers’ point of
view. Katz (2004) in his response from a practitioner’s point of view agreed with
Pullman’s. However, he concluded that the two methods are better used sequentially
and that QFD should precede conjoint analysis. We test these results in a market
for elderly people
Key words: Conjoint analysis, Quality function deployment, new product design,
elderly people
References
Baier, D. and Brusch, M. (2005): Linking Quality Function Deployment and Conjoint
Analysis for New Product Design. In: D. Baier, R. Decker and L. Schmidt-
Thieme (Eds.): Data Analysis and Decision Support. Springer, Berlin, 189-198.
Katz, G.M. (2004): A Response to Pullman et al.’s (2002) Comparison of Quality
Function Deployment versus Conjoint Analysis. Journal of Product Innovation
Management, 21, 61-63.
Pullman, M.E.,Moore, W.L. and Wardell, D.G.(2002): A Comparison Quality Function
Deployment and Conjoint Analysis in New Product Design. The Journal
of Product Innovation Management, 19, 354-364.
− 1 −

Joint Procrustes Analysis with Constrained
Simplimax Rotation: Nonsingular
Transformation of Component Score and
Loading Matrices Toward Simple Structure
Kohei Adachi
Graduate School of Human Sciences
Osaka University, Japan
Abstract. The solution of component analysis has indeterminacy on nonsingular
transformation: post-multiplying component score and loading matrices by a nonsingular
matrix and its transposed inverse matrix, respectively, does not change the
goodness of fit. To obtain a nonsingular matrix which gives simple structure to both
the score and loading matrices transformed, we propose joint Procrustes analysis
with constrained simplimax rotation, which consists of two phases. First, score and
loading matrices are rotated orthogonally so as to match the target score and loading
matrices, respectively, which include the elements of zeros, where the number of zero
elements is predetermined, but their placement and the values of non-zero elements
are unknown. Second, with the placement of the zero elements fixed at the result in
the first phase, a nonsingular matrix is obtained which matches transformed score
and loading matrices to the target score and loading matrices, respectively, where
the values of non-zero elements are unknown. This procedure is argued to be useful
for the cases where score and loading matrices have symmetric roles, for example,
a case where component analysis is performed for a data matrix of input signals by
output responses.
References
Adachi, K. (2005). Simultaneous Procrustes transformation of components and loadings
obtained from three-way data. P. 77 in http://www.psychometrika.org/
PDFs/IMPS2005_Abstracts.pdf.
Kiers, H. A. L. (1994). Simplimax: Oblique rotation to an optimal target with simple
structure. Psychometrika, 59, 567-579.
− 2 −

WaldIS - a web based reference system for the
forest monitoring in North Rhine-Westphalia
Christian Aden 1 , Hans Mucha 2 , Gunther Schmidt 1 , and Winfried Schröder 1
1 Lehrstuhl für Landschaftsökologie, Hochschule Vechta, D-49377 Vechta,
Germany, caden@iuw.uni-vechta.de
2 Weierstraß-Institut für Angewandte Analysis und Stochastik (WIAS),
D-10117 Berlin, Germany, mucha@wias-berlin.de
Abstract. In Germany, a multi-level forest monitoring was established since the
middle of the 1980ies. This hierarchical monitoring system consists of: the annual
forest condition surveys, the forest soil survey, and the intensive long term monitoring
of forest ecosystems. In North Rhine-Westphalia, these forest monitoring programmes
are supplemented by the monitoring of the foliar chemistry. Within theses
programmes, the monitoring data are recorded and evaluated by the respective federal
authorities, separately. An integrative statistical analysis of all data collected in
the different monitoring programmes could not be realised yet. To overcome these
constraints, the German Research Foundation (DFG) sponsors a research project
that aims at the compilation of the monitoring data by use of WebGIS techniques
and at integrated statistical analyses by use of geostatistics, time series analysis and
multivariate statistics. Currently, the reference data system WaldIS is being developed
for integrating data of the surveys interactively and for visualising the data
via a WebGIS. In addition, tools for both logical data queries and for downloads,
and some GIS functions were included. WaldIS was realised by using open source
software components instead of proprietary software: the UMN Mapserver was combined
with the WebGIS Client Suite Mapbender and the database management
system PostgreSQL. Furthermore, WaldIS relies on standards for processing geoobjects
published by the Open Geospatial Consortium (Pesch et. al 2007). Moreover,
WaldIS will be used to visualise the statistical results such as clusters or principal
components. With the help of stable statistical analysis based on rank-order data
the aim is finding areas of homogeneous environmental and forest conditions.
Key words: forest monitoring, WebGIS, multivariate rank analysis, stability
References
Pesch, R., Schmidt, G., Schröder, W., Aden, C., Kleppin, L. and Holy, M. (2007):
Development, Implementation and Application of the WebGIS MossMet. In:
A. Scharl and K. Tochtermann (Eds.): The Geospatial Web. Springer, London,
191–200.
− 3 −

Classification of Paired Data Using Ensemble
Methods
Werner Adler 1 , Alexander Brenning 2 , and Berthold Lausen 1
1
Chair for Biometry and Epidemiology, University of Erlangen-Nuremberg,
Germany
werner.adler@imbe.imed.uni-erlangen.de,
berthold.lausen@rzmail.uni-erlangen.de
2
Department of Geography, University of Waterloo, Canada
brenning@fesmail.uwaterloo.ca
Abstract. In glaucoma classification, the underlying data have a paired structure
that often is accounted for by simply using only one eye per subject. Brenning and
Lausen (2008) showed that the proper use of both eyes in paired cross-validation
decreases the variance of the estimation, compaired to cross-validation using only
one eye per subject.
We discuss and compare different strategies to generate the bootstrap samples
for training Adaboost (Freund and Schapire, 1996), Random Forest (Breiman, 2001),
and Double Bagging (Hothorn and Lausen, 2005). The simplest approach is to ignore
the paired data structure and proceed as usual. Adapting the idea by Brenning and
Lausen, we also perform subject based sampling. In a first step, subjects are drawn
with replacement. In a second step, for each drawn subject either both eyes or
one randomly selected eye are chosen, or two eyes are drawn with replacement.
The subjects not selected for training the base learners constitute the out-of-bag
samples. We compare error rates resulting from these different approaches obtained
by a simulation study.
Key words: Bootstrap, Classification, Glaucoma, Paired Organs
References
Breiman, L. (2001): Random forests. Machine Learning, 45, 5–32.
Brenning, A. and Lausen, B. (2008): Estimating error rates in the classification of
paired organs. Statistics in Medicine, submitted.
Freund, Y. and Schapire, R. (1996): Experiments with a new boosting algorithm.
Proceedings of the 13th International Conference on Machine Learning, 148–
156.
Hothorn, T. and Lausen, B. (2005): Bundling classifiers by bagging trees. Computational
Statistics & Data Analysis, 49, 1068–1078.
− 4 −

Segmentation of Neural Tissue
Bjoern Andres 1 , Ullrich Koethe 1 , Moritz Helmstaedter 2 , Winfried Denk 2 ,
and Fred Hamprecht 1
1 Interdisciplinary Center for Scientific Computing, University of Heidelberg
2 Max Planck Institute for Medical Research, Heidelberg
Abstract. Three-dimensional electron-microscopic image stacks with almost isotropic
resolution allow, for the rst time, to determine the com- plete connectivity matrix
of parts of the brain. In spite of major advances in staining, correct segmentation
of these stacks remains challenging, be- cause very few local mistakes can lead to
severe global errors. We propose a hierarchical segmentation procedure based on
statistical learning and topology-preserving grouping. First, edge probability maps
are computed by a random forest classier, and are partitioned into supervoxels by
the watershed transform. Over-segmentation is then resolved by constructing an irregular
graphical model on these supervoxels and inferring the most likely global
segmentation. Careful validation shows that the results of our algorithm are close
to human labelings.
− 5 −

On the power of corrected score functions to
adjust for measurement error
Thomas Augustin and Matthias Wallner
Department of Statistics, University of Munich (LMU)
augustin@stat.uni-muenchen.de
Abstract. Measurement error modeling, also called errors-in-variables-modeling,
is a generic term for all situations where additional uncertainty in the variables
has to be taken into account, in order to avoid severe bias in the statistical analysis.
The problem is omnipresent in technical statistics, when data from imperfect
measurement instruments are analyzed, as well as in biometrics, econometrics or
social science, where operationalizations (surrogates) are used instead of complex
theoretical constructs.
After a brief introduction to the area of measurement error modelling, the talk
discusses the power and some limitations of Nakamura’s general principle of corrected
score functions, mainly in the context of failure time data. Starting with classical
covariate measurement error in Cox’s PH model, it is shown how the Breslow
likelihood can be corrected, while according to results by Stefanski and Nakamura
himself no corrected score function for the partial likelihood can exist. We then turn
to parametric failure time models and extend consideration to additionally errorprone
lifetimes. Finally, some ideas for handling Berkson-type errors (as occurring,
e.g., in Radon studies) and rounded errors will be sketched.
Key words: Measurement error, error-in-variables, survival analysis, Cox model,
rounding
− 6 −

Evaluation Criteria for the Construction of
Binary Classification Trees with Two or More
Classes
Muhammad Azam 1 Alexander Ostermann 2 and Karl-Peter Pfeiffer 3
1 Department of Medical Statistics, Informatics and Health Economics, Medical
University Innsbruck csag2533@uibk.ac.at
2 Institute for Mathematics, Unversity of Innsbruck , Technikerstrasse 25/7, 6020
Innsbruck alexander.ostermann@uibk.ac.at
3 Department of Medical Statistics, Informatics and Health Economics, Medical
University Innsbruck Karl-Peter.Pfeiffer@i-med.ac.at
Abstract. Classification trees are top-down induction of labelled sampling units
into recursive order to get end nodes. Each end node representing those labelled
units which are in majority, otherwise considered as misclassified. In the top-down
induction process, an evaluation criterion plays an important role to send maximum
of the units having same label to the same node. To achieve this goal a ”goodness
of split” measure is calculated by using evaluation criteria e.g. Gini function, Twoing
rule etc. for each distinct value of each variable and finally chooses one which
enhances the purity. For smaller number of classes attached to all the units, almost
all the evaluation criteria provides the same results in terms of misclassified units,
deviance and number of end nodes but it matters for larger number of classes and
considered best criteria which provides less number of misclassified units especially.
Here we proposed an impurity based evaluation criteria which fulfil all the required
properties of any evaluation criteria (Breiman et al., 1984). (i) The node impurity
function achieves its maximum value, when same number of units fall in a node belongs
to J number of classes. (ii) Node is pure, when all the observations of a node
belong to a single class. (iii) A node impurity function is a symmetric function.
We conducted a simulation study to test the performance of the proposed criterion
over many real life datasets available under UCI repository and observed that the
proposed strategy provides improved results.
Key words: Classification trees, Evaluation criteria, Misclassification rate, Deviance
References
Breiman, L., Friedman, J. H., Olshen, R. A. and Stone, C. J (1984): Classification
and regression trees. Wadsworth International Group, Belmont, CA.
− 7 −

Evaluation Strategies for Learning Algorithms
of Hierarchical Structures
Korinna Bade 1 and Dominik Benz 2
1 Faculty of Computer Science, Otto-von-Guericke-University Magdeburg,
D-39106 Magdeburg, Germany, Email: korinna.bade@ovgu.de
2 Department of Electrical Engineering/Computer Science, University of Kassel,
D-34121 Kassel, Germany, Email: benz@cs.uni-kassel.de
Abstract. The idea to automatically induce a hierarchical structure among a set
of objects or integrate a given hierarchy into the learning process is common to
a number of disciplines like hierarchical clustering (Bade and Nürnberger, 2008)
and classification or ontology learning. A crucial aspect hereby is how to assess the
quality of the learned hierarchical scheme. Existing evaluation approaches can be
broadly classified in methods defining quality metrics on the resulting scheme alone
and methods which invoke an external “gold-standard” for comparison. We focus
on the latter case, for which various similarity metrics have been proposed, mostly
depending on the characteristics of the applied learning procedure.
This work aims at bringing together the different disciplines by presenting and
comparing existing gold-standard based evaluation methods for learning algorithms
that generate hierarchical structures. We present an interdisciplinary framework in
order to enable comparison across the different contexts, from which the metrics
originate. Our goal is to emphasize the strong similarities of evaluation tasks in different
disciplines and to create a general pool of evaluation methods. Based on prior
work (Dellschaft and Staab, 2006), we analyze properties of (good) evaluation measures.
Different types of structural errors in the learned hierarchies are identified and
their effects on existing measures are shown. Observing strengths and weaknesses of
existing methods, we also suggest some new methods.
Key words: evaluation metrics, hierarchical clustering, ontology learning, goldstandard
References
Bade, K. and Nürnberger, A. (2008): Creating a Cluster Hierarchy under Constraints
of a Partially Known Hierarchy. In: Proceedings of the 2008 SIAM International
Conference on Data Mining. (to appear)
Dellschaft, K. and Staab, S. (2006): On How to Perform a Gold Standard Based
Evaluation of Ontology Learning. In: Proc. of 5 th Int. Semantic Web Conference.
228–241.
− 8 −

Autonomous Forex Trading Agents
Rui Pedro Barbosa 1 and Orlando Belo 2
1
Department of Informatics, University of Minho, 4710-057 Braga, Portugal
rui.barbosa@di.uminho.pt
2
Department of Informatics, University of Minho, 4710-057 Braga, Portugal
obelo@di.uminho.pt
Abstract. Trading in financial markets is undergoing a radical transformation,
one in which algorithmic methods are becoming increasingly more important. The
development of intelligent agents that can act as autonomous traders of financial
instruments seems like a logical step forward in this “algorithms arms race”. With
this in mind, our study proposes an infrastructure for implementing hybrid intelligent
agents with the ability to trade in the Forex Market without requiring human
supervision. This infrastructure is composed of three modules. The Intuition Module,
implemented using an Ensemble Model, is responsible for performing pattern
recognition and predicting the direction of the exchange rate. The A Posteriori
Knowledge Module, implemented using a Case-Based Reasoning System, enables
the agents to learn from empirical experience and is responsible for suggesting
how much to invest in each trade. Finally, the A Priori Knowledge Module, implemented
using a Rule-Based Expert System, enables the agents to incorporate
non-experiential knowledge in their trading decisions. This infrastructure was used
to implement two agents, one capable of trading the USD/JPY currency pair, and
the other one capable of trading the EUR/USD currency pair, both with a 6 hours
timeframe. Using 12 months of out-of-sample data, the USD/JPY agent performed
826 simulated trades and obtained an average profit per trade of 6.88 pips. It accurately
predicted the direction of the price in 54.72% of the trades, 65.74% of which
were profitable. Over the same period, the EUR/USD agent performed 885 trades,
with an average profit of 6.06 pips per trade. Its accuracy predicting the direction
of the price was 52.99%, and 60.45% of its trades were profitable. These agents were
integrated with an Electronic Communication Network and have been trading live
for the past several months. So far their live trading results are consistent with the
simulated results, which lead us to believe our infrastructure can be of practical
interest to the traditional trading community.
Key words: Forex trading, Hybrid agents, Autonomy
− 9 −

Improving Product Line Design with Bundling
Niels Becker and Brigitte Werners
Faculty of Economics and Business Administration,
Ruhr-University Bochum, 44780 Bochum, Germany
niels.becker@ruhr-uni-bochum.de and or@ruhr-uni-bochum.de
Abstract. Designing and pricing new products is of particular importance in many
industries. In order to meet heterogeneous customer needs, many companies offer different
variants of every product type. To support these product line design decisions,
various mathematical programming approaches have been developed (Steiner and
Hruschka, 2003). Most models are based on part-worth utilities, estimated within a
conjoint framework. Besides, bundling is an important tool in marketing. It has been
shown that bundling can transfer customers’ willingness to pay from one product to
another. Therefore, prices can be differentiated so that higher profits are obtainable
(Simon and Wübker, 1999). For determining optimal bundles and prices, Hanson
and Martin (1990) have suggested a well-known linear programming model.
Here the problem of optimally designing, bundling and pricing new products
is investigated. One of the questions is, at which point in time bundling decisions
should be made. Therefore, we compare product line design without bundling with
sequential bundling, which means bundling subsequent to product line decisions, and
simultaneous bundling, which means determining optimal bundles and product lines
simultaneously. We developed a combined product line design and bundling model
and present the impact on profits using simulated data. For this example, optimal
results can be obtained using MILP-Software. Our studies show that simultaneous
bundling leads to differently designed products and can improve profits substantially.
Key words: Product Line Design, Pricing, Bundling, Optimization
References
Hanson, W. and Martin, K. (1990): Optimal Bundle Pricing. Management Science,
36(2), 155–174.
Simon, H. and Wübker, G. (1999): Bundling - A Powerful Method to Better Exploit
Profit Potential. In: R. Füderer, A. Hermann and G. Wübker (Eds.): Optimal
Bundling, Springer-Verlag, Heidelberg-Berlin, 7–28.
Steiner, W. and Hruschka, H. (2003): Genetic Algorithms for Product Design: How
Well Do They Really Work. Int. Journal of Market Research, 45(2), 229–240.
− 10 −

Estimating the number of buildings in
Germany
Martin Behnisch 1 and Alfred Ultsch 2
1 Institute of Historic Building Research and Conservation, ETH Hoenggerberg,
HIL D 25.9, CH-8093 Zurich. Behnisch@arch.ethz.ch
2 Datenbionic Research Group, Hans-Meerwein-Strasse, Philipps-University
Marburg, D-35032 Marburg. Ultsch@Mathematik.Uni-Marburg.de
Abstract. The building stock can be considered the largest physical, economical
and cultural capital of a society. For German building stocks many institutions
record different kind of data. Unfortunately there are just a few basic statistics
about the amount of buildings. Collection of data is therefore very complicated,
often expensive and the handling of missing data is one of the biggest handicaps.
With the exception of data about residential buildings and particularly monuments,
it is an unsolved problem to determine the total number of buildings. The main
issue of this article is the description of an estimation procedure for this. Using
methods from the, so called, Urban Knowledge Discovery approach, the authors find
unsuspected relationships in the urban data which can be used for the estimation.
The developed estimation procedure relies on 12430 municipalities and refers to data
from the Cadaster of Real Estates and the Federal Bureau of Statistics. With this
estimation it is possible to use statistical data from well known and easily accessible
institutions. The number of buildings is estimated for regions with missing data.
The quality of the estimation is analyzed by learn and test data sets. Information
optimization leads to the conclusion that 20% of the municipalities hold 80% of all
buildings. Therefore for an improvement of the estimation it is essential to refine
the amount and quality of data in the larger municipalities.
Key words: Spatial Planning, Engineering, Knowledge Discovery, Data Mining,
Building Stock
References
Aachener Institut füer Bauschadensforschung und Angewandte Bauphysik, Hrsg:
Hofman, F. (2001): Urban heritage - building maintenance. Final report. COST
Action C5, European Commission.
Becher, St. (1995): Klassifikation der regionalen Immobilienmärkte der Bundesrepublik
Deutschland (Dissertation). Universität, Mainz.
Behnisch, M. (2007): Urban Knowledge Discovery (Doctoral thesis). Universitätsverlag,
Karlsruhe.
− 11 −

Probabilistic Distance Clustering
Adi Ben-Israel
Rutgers University, RUTCOR
Summary. A new iterative method [1] for probabilistic clustering of data is presented.
Given clusters, their centers, and the distances of data points from these
centers, the probability of cluster membership at any point is assumed inversely
proportional to the distance from (the center of) the cluster in question.
The resulting method is a generalization, to several centers, of the Weiszfeld
method for solving the Fermat–Weber location problem. At each iteration, the
distances (Euclidean, Mahalanobis, etc.) from the cluster centers are computed for
all data points, and the centers are updated as convex combinations of the data
points, with weights determined by the above principle. Computations stop when
the centers stop moving.
This approach works also for problems where the cluster sizes are unknowns (to
be estimated), giving a viable alternative to the EM method, [2].
Progress is monitored by the joint distance function (JDF), a measure of
distance from all cluster centers, that evolves during the iterations, and captures the
data in its low contours. This is a new concept in data reduction and representation.
A duality theory for the JDF is given in [3].
The method is simple, fast (requiring a small number of cheap iterations) and
is not sensitive to outliers.
Key words: Partial Least Squares, Correspondence Analysis, Categorical Data
References
A. B-I and C. Iyigun, Probabilistic Distance Clustering, J. Classification (to
appear.) http://benisrael.net/J-CLASSIFICATION-07.pdf.
C. Iyigun and A. B-I, Probabilistic Distance Clustering adjusted for Cluster
Size, Probability in Engineering and Informational Sciences (to appear.)
http://benisrael.net/PEIS-07.pdf.
C. Iyigun and A. B-I, Contour Approximation of Data: A Duality Theory, (submitted.)
http://benisrael.net/DUAL-12-20-07.pdf.
− 12 −

Hedge Funds in a Bayesian Asset Allocation
Framework: Incorporating information on
market states and manager’s ability
Wolfgang Bessler 1 and Julian Holler 2
1 Center for Finance and Banking, Licher Strasse 74, 35394 Giessen
Wolfgang.Bessler@wirtschaft.uni-giessen.de
2 Center for Finance and Banking, Licher Strasse 74, 35394 Giessen
Julian.Holler@wirtschaft.uni-giessen.de
Abstract. A growing number of private and institutional investors make significant
allocations to hedge funds in order to improve the risk-return trade-off of their portfolios.
However, in a portfolio context there are a number of issues that are special
to hedge funds. We attempt to address these issues in a bayesian asset allocation
framework (Pastor 2000). In particular, we focus on the returns of two representative
equity hedge fund strategies constructed by replication of two well-known
statistical arbitrage strategies. Importantly, this approach allows us to obtain daily
return observations despite the fact that most funds only report at a monthly interval.
Using this framework, we investigate the following two research questions.
First, we address the issue that many arbitrage strategies exhibit substantial exposures
to financial crisis which is reflected in their high levels of curtosis and negative
skewness. By including relevant state variables in the prior distribution, we infer
whether investors can improve the risk-adjusted performance of their portfolios by
reducing their exposures prior to the onset of a crisis. Second, investors should only
pay high fees to hedge fund managers if they earn additional alpha above the returns
generated by our dynamic trading strategy. Thus, we attempt to analyze how much
confidence an investor has to put into a manager’s abilities by varying her prior
beliefs about alpha.
References
Pastor, L. (2000): Portfolio Selection and Asset Pricing Models. Journal of Finance,
55, 179–223
Keywords
Asset Allocation, Alternative Investments, Hedge Funds
− 13 −

Categorical Data in PLS Path modeling
Jörg Betzin
German Centre of Gerontology (DZA), Berlin, Germany
joerg.betzin@dza.de
Summary. There are lot of surveys with categorical data where the relationships
between the variables should be used in a path model with latent variables but, so
far there are only a few possibilities to do so. We present a way using categorical
manifest variables (MV’s) in PLS.
The main idea is on the one hand in thinking of PLS as a generalization of PCA
(principal component analysis) or canonical correlation and on the other hand using
the framework of Correspondence Analysis (CorA) as a generalization of PCA for
categorical variables and put these two approaches together.
In the basic PLS algorithm the latent variables (LV’s) ηm (m = 1, ..., M) are
estimated as weighted sum of their manifest variables (with data matrices Ym)
ηm = Ymωm, where the pooled weight vector ω = (ω ′ 1, ..., ω ′ M ) ′ is result of an
iteration algorithm like
ω = `` Y ′ Y ´ ∗ P ´ ω
with additional normalization constraint and where Y = (Y1, ...,YM ), P is a weight
matrix changing with different iteration steps, and ’∗’ indicating the elementwise
matrix product. The key point is the use of the covariance matrix Y ′ Y inside the
iteration algorithm.
Now, one main aspect of CorA is the transformation of the raw data matrix Ym
into an indicator matrix Gm and the analysis of a kind of correlation matrix for Gm.
If we describe by e Qm a suitable transformation of Gm where elements of e Q ′ m e Qm are
roots of χ 2 -components from twodimensional contingency tables for columns in Gm.
Than, in short, e “ ”
Q = eQ1, ..., QM e will be used as an equivalent for the covariance
matrix Y ′ Y in the PLS iteration algorithm.
We will show results for different examples using basic PLS algorithms and
using PLS algorithms adopted for categorical manifest variables, together with interpretations
of the weights ωm in the case of categorical data and the other model
parameters like correlations and regression coefficients.
Key words: Partial Least Squares, Correspondence Analysis, Categorical Data
− 14 −

Choosing the number of clusters in the latent
class model
Cristophe Biernacki 1 , Gilles Celeux 2 , and Gérard Govaert 3
1 Université Lille 1 UMR CNRS 8524, France
Christophe.Biernacki@math.univ-lille1.fr
2 INRIA Saclay, France Gilles.Celeux@inria.fr
3 UTC Compiègne UMR CNRS 6599 Heudiasyc, France Gerard.Govaert@utc.fr
Abstract. The latent class model or multivariate multinomial mixture is a powerful
model for clustering discrete data. This model is expected to be useful to represent
nonhomogeneous populations. It uses a conditional independence assumption given
the latent class to which a statistical unit is belonging. In this presentation, we exploit
the fact that a fully Bayesian analysis of the latent class model with Jeffreys
non informative prior distributions does not involve technical difficulty to derive
the exact integrated complete likelihood. Then, we exploit this integrated complete
likelihood as a criterion to assess the number of mixture components in a cluster
analysis perspective. We highlight with numerical experiments how this exact criterion
could outperfom the BIC-like asymptotic approximation generally used to
choose a sensible number of clusters derived from the latent class model.
Key words: Latent Class model, Integrated complete likelihood, Model selection
References
Biernacki, C., Celeux, G. and Govaert, G. (2000). Assessing a mixture model for
clustering with the integrated completed likelihood. IEEE Trans. on PAMI,
22, 719-725.
Goodman, L. A. (1974). Exploratory latent structure analysis using both identifiable
and unidentifiable models. Biometrika, 61, 215-231.
− 15 −

Clustering of molecules and structured data
Gilles Bisson
Laboratoire TIMC-IMAG - Equipe AMA , Faculté de Médecine
Summary. The discovery or the synthesis of molecules that activate or inhibit some
biological systems is a central issue for biological research and health care. The objective
of High Throughput Screening (HTS) is to rapidly evaluate, through automated
approaches, the activity of a given collection of molecules on a given biological target
that can be an enzyme or a whole cell. In practice, the results of a HTS test allow to
highlight some tens of active molecules, named the ”hits”, representing a very small
percentage of the initial collection. However, these tests are just the beginning of the
work since the identified molecules generally do not have some nice characteristics in
terms of sensitivity and specificity (a relevant molecule must by specific to the biological
target and should be efficient with a small concentration). In such context, it
is crucial to provide the chemists with some tools to explore the contents of his/her
chemical libraries and especially to make easier the search for molecules that are
structurally similar to the ”hits”. A possible approach, given a relevant distance, is
to seek the nearest neighbours of those hits. More broadly, chemists have a need for
methods to automatically organize the collections of molecules in order to locate the
active molecules within the chemical space. Above all, they would like to evaluate
the real diversity of the chemical structures contained in a collection. Clustering
methods are well suited to carry out this kind of task. However, with structurally
complex objects such as molecules, it is obvious that the quality of the results depends
on the capacity of the distance used by the clustering method to grasp the
structural similarities and also to take into account all the background knowledge
of the chemists. The search for a structural distance between molecules is clearly
related (but not totally equivalent) to the search for isomorphic partial subgraphs,
which is a NP-complete problem. To overcome this problem many methods use an
”a priori” molecular linearization: a molecule is represented by a vector of descriptors,
each one corresponding to a molecular fragment, and well-known distances can
be used. However, since last ten years, kernel functions, comparable to distances
between graphs, have been proposed in the Support Vector Machines framework.
In these approaches, molecular representation is more accurate. It can be based on
a set of path (i.e. molecular fragments specifically chosen or randomly selected), or
more interestingly use the whole molecule to try to value structural distances by
dynamically explore the mapping that can be done between two molecules.
− 16 −

The K -INDSCAL Model for Heterogeneous
Three-way Dissimilarity Data
Laura Bocci 1 and Maurizio Vichi 2
1 Department of Sociology and Communication, University of Rome “La
Sapienza”, Rome, Italy laura.bocci@uniroma1.it
2 Department of Statistics, Probability and Applied Statistics, University of Rome
“La Sapienza”, Rome, Italy maurizio.vichi@uniroma1.it
Abstract. The weighted Euclidean model proposed by Carrol and Chang (1970) is
the most well-known and used model of multidimensional scaling of three-way data.
INDSCAL states a unique representation for the objects (common configuration
space) and for each occasion weights for the dimensions of this representation (individual
differences weights), thus definitely assuming that there are not systematic
“strong” differences between data dissimilarity sources. Otherwise, when heterogeneous
occasions are observed, it is shown that INDSCAL may fail to identify a
common space representative of the observed data structure. In such frequent and
realistic situation it is reasonable to assume that there are systematic differences
among some, say, K clusters of occasions in the evaluation of the dissimilarities,
so that within each cluster of occasions the evaluation may differ only because of
sampling or measurement errors; while between clusters of occasions dissimilarities
are really different. The heterogeneous INDSCAL in K classes model, simply called
K -INDSCAL, is proposed to handle the above described heterogeneity in the data.
The model includes the individual weights in order to preserve the rotational invariance
of the INDSCAL model. The high number of parameters of INDSCAL, and
consequently of K -INDSCAL, may produce instability of the estimates thus a parsimonious
model, that drastically reduces the number of parameters, is also discussed.
The parameters of the model are estimated in a least-squares fitting context and an
efficiently coordinate descent algorithm is given. The usefulness of K -INDSCAL is
demonstrated by both artificial and real data analyses.
Key words: Three-way dissimilarity data, INDSCAL, heterogeneous data dissimilarities
References
Carroll, J.D. and Chang, J.J. (1970): Analysis of individual differences in multidimensional
scaling via an N-generalization of the Eckart-Young decomposition.
Psychometrika, 35, 283–319.
− 17 −

Weighting and Selecting Features
in Fuzzy Clustering
Christian Borgelt
European Center for Soft Computing
c/ Gonzalo Gutiérrez Quirós s/n, 33600 Mieres, Spain
christian.borgelt@softcomputing.es
Abstract. A serious problem in distance-based clustering is that the more dimensions
(attributes) a datasets has, the more the distances between data points—and
thus also the distances between data points and constructed cluster centers—tend to
become uniform. This, of course, impedes the effectiveness of clustering, as distancebased
clustering exploits that these distances differ. In addition, in practice often
only a subset of the available attributes is relevant for forming clusters, even though
this may not be known beforehand. In such cases it is desirable to have a clustering
algorithm that automatically weights the attributes or even selects a proper subset.
In this contribution I study the problem of weighting and selecting features in
clustering and in particular in fuzzy clustering. Apart from reviewing straighforward
modifications of Gustafson–Kessel fuzzy clustering (Gustafson and Kessel 1979) and
attribute weighting fuzzy clustering (Keller and Klawonn 2000) that lead to simple,
but effective attribute weighting schemes, I introduce a new feature selection method
by applying the idea of an alternative to the fuzzifier (Klawonn and Höppner 2003)
to the latter scheme. The resulting combined feature weighting and selection method
has the advantage that the obtained clustering result on the chosen subspace coincides
with the projection of the result obtained on the full data space. Finally I
discuss an extension of this scheme to principal axes selection.
Key words: fuzzy clustering, feature weighting, feature selection
References
1.Gustafson, E.E., and Kessel, W.C. (1979): Fuzzy Clustering with a Fuzzy Covariance
Matrix. Proc. IEEE Conf. on Decision and Control (CDC 1979, San
Diego, CA), 761–766. IEEE Press, Piscataway, NJ, USA.
2.Keller, A., and Klawonn, F. (2000): Fuzzy Clustering with Weighting of Data
Variables. Int. Journal of Uncertainty, Fuzziness and Knowledge-based Systems
8:735-746. World Scientific, Hackensack, NJ, USA.
3.Klawonn, F., and Höppner, F. (2003): What is Fuzzy about Fuzzy Clustering?
Understanding and Improving the Concept of the Fuzzifier. Proc. 5th Int.
Symposium on Intelligent Data Analysis (IDA 2003, Berlin, Germany), 254–
264. Springer-Verlag, Berlin, Germany.
− 18 −

HIDDEN MARKOV MODEL BASED
CLASSIFICATION OF NATURAL OBJECTS
IN AERIAL PICTURES
Mohamed El Yazid Boudaren 1 , Abdenour Labed 1 , Adel Aziz Boulfekhar 1 ,
and Yacine Amara 1
Military Polytechnic School, Algiers boudarenyazid@hotmail.com
Abstract. This work is part of a more global one that consists in creating virtual
environments from aerial pictures combined with altimetry data. In such environments,
while getting too close to the ground, one has to solve the problem of limited
texture resolution. So, these textures have to be amplified to get more realistic
scenes. Texture amplification must take account of object nature. This paper deals
with picture pixels supervised classification in order to amplify texture resolution.
For this purpose, we propose a hidden Markov model based approach that takes
into account the spatial dependencies between natural objects present in the area
of interest. HMMs have long been used to efficiently model one-dimensional data,
in particular in speech recognition systems. In theory, HMMs can be applied as
well to multi-dimensional data. However, the complexity of the algorithms grows
exponentially in higher dimensions, so that, even in dimension 2, the usage of plain
HMM becomes prohibitive in practice. To overcome the 2D-HMM complexity, we
propose a two-level HMM, where the higher layer comprises one unique HMM constituted
of super states associated to one low level HMM each. Our model differs
from classic embedded HMM in that it deals with pixel blocks instead of pixel lines
as elementary symbols. Another difference is that our high level HMM is ergodic;
this enables our model to accurately model spatial dependencies between natural
objects. The training of our HMM models is done in two steps: firstly, the low level
HMMs are trained on unitextured pictures. Secondly the high level one is trained
on multitextured pictures of the same region using the parameters of HMMs of the
first step, according to Baum-Welch algorithm with slight modifications. For our
experiments, we used real world aerial pictures of a relatively large area, with a resolution
of 50 centimeters. Our results were then used to generate virtual interactive
3D-scene. This showed that our classifier was able to satisfactorily reproduce the
original terrain.
Key words: Hidden Markov models, Aerial pictures supervised classification, texture
recognition
− 19 −

On optimistic bias in reporting
microarray-based classification accuracy
Anne-Laure Boulesteix 1 and Martin Slawski 1
1 Sylvia Lawry Centre for Multiple Sclerosis Research, Hohenlindenerstr. 1,
81677-München, Germany, boulesteix@slcmsr.org
Abstract. Almost all published medical studies present positive research results.
In the special case of microarray studies, which often focus on, e.g., the identification
of differentially expressed genes or the construction of outcome prediction
rules, it means that almost all studies report at least a few significant differentially
expressed genes or a small prediction error, respectively. Authors are virtually urged
to “find something significant” in their data, which encourages the publication of
wrong research findings due to the multiple comparison effects. If authors try a large
number of different analysis methods and designs on their data, they are likely to
obtain “acceptable results” with at least one of them. Microarray-based class prediction
is particularly affected by this problem. Whereas logistic regression is routinely
applied as the standard class prediction approach in the simple case where only a
small number of predictors are available, there is no consensus on the procedure to
be applied for classification using high-dimensional microarray data.
It is well-known that, if several statistical methods are tried on the same microarray
data set, one should report all results, not only the best ones (Dupuy and
Simon, 2007). Through simulations and real data studies, we address this problem
quantitatively and determine the effect of not respecting this “good practice” rule.
Our approach consists of applying a large number of well-known classifiers combined
with several variable selection procedures and different numbers of selected variables,
and evaluating them following different schemes (see Boulesteix et al, 2008, for an
overview). The considered data sets are real publicly available microarray data sets,
with or without random permutation of the class labels. The output of our study is
the distribution of the minimally selected error rate and the bias resulting from this
optimal selection in the different settings.
References
Boulesteix, A.-L., Strobl, C., Augustin, T. and Daumer, M. (2008): Evaluating
microarray-based classifiers: An overview. Cancer Informatics, 4.
Dupuy, A. and Simon, R. (2007): Critical Review of Published Microarray Studies for
Cancer Outcome and Guidelines on Statistical Analysis and Reporting. Journal
of the National Cancer Institute, 99, 147–157.
− 20 −

Practical experiences from Credit Scoring
projects for Chilean financial organizations.
Cristian Bravo, Sebastian Maldonado and Richard Weber
Department of Industrial Engineering, University of Chile.
cbravo@dii.uchile.cl, semaldon@ing.uchile.cl, rweber@dii.uchile.cl
Abstract. All financial organizations that offer loans to their customers have the
problem to determine if the loaned money will be returned. Credit scoring systems
have been successfully applied to determine the probability that a certain customer
will fail in paying back the received credit. In many cases these systems are based
on experience but offer a “closed solution” where the user has few possibilities to
influence in the decision process.
We have developed credit scoring systems for several Chilean financial organizations
mapping the KDD process (Knowledge Discovery in Databases) to their
special needs. This paper presents our experiences from these projects and explains
in detail how we solved the problems in each step of the KDD process.
In the data mining step we applied Logistic Regression and Support Vector
Machines as classification techniques, comparing both their performance and their
flexibility. A particular wrapper approach for feature selection using Support Vector
Machines has been developed. Comparing this approach with alternative schemes
underlines its strengths in terms of classification performance and selected features.
Since most KDD projects propose just static solutions we had to develop a
module for model updating that will be described in detail. In particular we propose
to apply statistical techniques in order to determine changes in feature weights and
structural changes in the respective universe.
During the development of our solutions the users got important insights into
their customers’ behavior, some of them were surprising, others just confirmed notions
the respective experts had before. By using the systems in daily operation the
rate of false positives as well as false negatives could be reduced leading to a higher
coverage of the respective market.
Key words: Credit scoring, Classification, Support Vector Machines.
References
Famili, A., Shen, W.-M., Weber, R., Simoudis, E. (1997): Data Preprocessing and
Intelligent Data Analysis. Intelligent Data Analysis 1, No. 1, 3-23.
− 21 −

Analyzing the Stability of Price Response
Functions - Measuring the Influence of
Different Parameters in a Monte Carlo
Comparison
Michael Brusch and Daniel Baier
Institute of Business Administration and Economics,
Brandenburg University of Technology Cottbus, Postbox 101344,
D-03013 Cottbus, Germany {m.brusch|daniel.baier}@tu-cottbus.de
Abstract. The usage and therefore the estimation of price response function (see,
e.g., Steiner et al. 2007) is very important for strategic marketing decisions. Typically
price response functions with an empirical basis are used (see, e.g., Balderjahn
1998). However, such price response functions are subject to a lot of disturbing influence
factors, e.g. the assumed profit maximal price and the assumed corresponding
quantity of sales.
In such cases, the question how stable the found price response function is was
not answered sufficiently up to now. In this paper, the question will be pursued how
much (and what kind of) errors in market research are pardonable for a stable price
response function. Innovative technologies and systems of house power engineering
are used as application example (see Brusch et al. 2003). For the comparisons, a
factorial design with synthetically generated and disturbed data is used.
Key words: Monte Carlo comparison, Price response functions
References
Balderjahn, I. (1998): Empirical analysis of price response functions. In: I. Balderjahn,
C. Mennicken, E. Vernette (Eds.): New Developments and Approaches in
Consumer Behavior Research. Schäffer-Poeschel/Macmillan, 185–200.
Brusch, M., Zühlsdorff, D., Baier, D. and Kessler, A. (2003): Neue Technologien und
erneuerbare Energiequellen auf dem Vormarsch. Energiewirtschaftliche Tagesfragen,
53, 12, 825–829.
Steiner, W. J., Brezger, A. and Belitz, Ch. (2007): Flexible estimation of price
response functions using retail scanner data. Journal of retailing and consumer
services, 14, afl. 6 (11), 383–393.
− 22 −

Motif-based Classification of Time Series with
Bayesian Networks and SVMs
Krisztian Antal Buza 1 and Lars Schmidt-Thieme 1
University of Hildesheim, Information Systems and Machnine Learning Lab
{buza,schmidt-thieme}@ismll.uni-hildesheim.de
Abstract. Classification of time series is of crucial importance in wide range of
applications. One of the possible solutions for this problem is based on characteristic
local patterns of time series, so-called motifs [Patel 2002].
We present a novel technique to make the classification of (multivariate) time
series more accurate. We define different types of motifs. Most easy ones are frequent
subseries. In case of noisy time series as well as in several application domains these
easy motifs are not sufficient, more complex ones are necessary. Complex motifs
used in our work may consist of several subseries, continouos and non-continouos
parts and “joker” parts. We show an efficient algorithm for mining complex motifs in
time series. We extend the highly efficient implementation of the algorithm Apriori
described in [Borgelt 2003] to our task.
We evaluate our method on real medical data, which consits of time series of
dialysis sessions. We compare different types of motifs according to their ability for
the prediction of the class of (multivariate) time series. We show that additional
motif features significantly improve the accuracy of Bayesian Networks and Support
Vector Machines for the classification of time series.
Key words: Time Series, Complex motifs, Bayesian Networks, SVM
References
Borgelt, C. (2003): Efficient Implementations of Apriori and Eclat. 1st Workshop of
Frequent Item Set Mining Implementations (FIMI 2003, Melbourne, FL, USA).
Kunik, V. and Solan, Z. and Edelman, S. and Ruppin, E. and Horn, D. (2005): Motif
Extraction and Protein Classification. IEEE Computational Systems Bioinformatics
Conference (CSB’05), pp. 80-85
Ferreira, P. G. and Azevedo, P. (2005): Protein Sequence Classification through Relevant
Sequence Mining and Bayes Classifiers Proceedings of the 12th Portuguese
Conference on Artificial Intelligence, pp. 236-247, LNAI 3808, Springer-Verlag.
Patel, P. and Keogh, E and Lin, J. and Lonardi, S (2002): Mining Motifs in Massive
Time Series Databases. Proceedings of the 2002 IEEE International Conference
on Data Mining (ICDM 2002).
− 23 −

Visualizing data in Gaussian mixture model
classification
Daniela G. Calo’ and Cinzia Viroli
Department of Statistics - University of Bologna
via Belle Arti, 41 - 40126 Bologna, Italy
danielagiovanna.calo@unibo.it, cinzia.viroli@unibo.it
Abstract. The paper presents a post-processing strategy for producing low-dimensional
summary plots of the data after a Gaussian mixture classification model has been
fitted. The most revealing projections are those along which the class-conditional
densities are maximally separable. We consider a particular probability product kernel
as a measure of similarity or affinity between class-conditional distributions. It
takes an appealing closed form in the case of Gaussian mixture components. The
performance of the proposed strategy has been evaluated on simulated and real data.
Key words: Gaussian mixture models, Low-dimensional plots, Normalized expected
likelihood kernel, Bayes error
References
Chan, A.B., Vasconcelos, N. and Moreno, P.J. (2004): A family of Probabilistic
Kernels Based in Information Divergence. Technical Report, University of California,
San Diego.
Jebara, T. and Kondor, R. (2004): Probability Product Kernels. Journal of Machine
Learning Research, 5, 819–844.
McLachlan, G.J. and Peel, D. (2000): Finite Mixture Models. Wiley, New York.
− 24 −

A novel approach to construct discrete support
vector machine classifiers
Marco Caserta and Stefan Lessmann
Institute of Information Systems
University of Hamburg, Germany
Abstract. The support of managerial decision making by means of data mining
has received considerable attention in the academic literature as well as corporate
practice. This paper considers support vector machines (SVMs) which represent a
popular classification method that may be used in data mining to, e.g., guide the selection
of customers for a direct marketing campaign or assess the credibility of loan
applications in financial applications. Recently, Orsenigo and Vercellis proposed a
novel, discrete support vector machine (DSVM) and demonstrate its effectiveness in
several empirical studies. Building a respective classifier involves solving an integer
program which is a challenging computational task in general and in large-scale data
mining settings in particular. This paper strives to improve upon a linear programming
based heuristic, originally proposed by Orsenigo and Vercellis for solving the
DSVM program. The core of the suggested procedure consists of a recursive algorithm
that solves the (linear) relaxation of DSVM and exploits dual information to
construct a smaller sized sub-program with integer constraints that may be solved
to optimality. The sequence of linear and integer programs solved during the course
of the algorithm provides upper and lower bounds of the final solution which are
employed as termination criterion. Empirical experiments are conducted to scrutinize
the suitability of the proposed procedure and examine the problem size (i.e.
the number of examples and features) that can be processed with state-of-the-art
integer programming techniques.
− 25 −

Modeling the Classification of Heterogeneous
Data
Dorin Carstoiu, Tudor Ionescu, and Alexandra Cernian
University of Bucharest
Faculty of Automatic Control and Compuer Science
Bucharest, Romania
{dorin.carstoiu,tudor.ionescu,alexcernian}@yahoo.com
Abstract. The goal of this work is to study the feasibility of a Heterogeneous Data
Classification and Search (HDCS) system and to provide a possible design for its
implementing. In order to design a HDCS system we propose an actor oriented
modeling technique, for which we show the information flow. We have identified 6
different actors (subsystems) which collaborate to construct a file sheet and produce
the final search result. The first 5 actors add information to the files sheet, which is
afterwards used by the final actor to produce the desired result.
Given the vast quantity of data and the variety of formats and encodings it exists
in, a semantic approach based on metadata has been chosen. Instead of digging into
the actual data for extracting information, we used the context of the file to collect its
metadata. The metadata is afterwards used for the classification proces. The reason
for this approach is that data are made available by people who are interested in
other people understanding what the respective data are about. This observation
provided the confidence needed to pursue the presented approach.
The HDCS system we propose combines techniques from conventional search
systems, classification systems, search results clustering systems, while also providing
original solutions, such as an innovative data sampling method.
References
Dawid Weiss: Descriptive Clustering as a Method for Exploring Text Collections,
2006
Frederic Boulanger, Guy Vidal-Naquet: A Primitive Execution Model for Heterogeneous
Modeling
Mokhoo Mbobi, Frederic Boulanger, Mohamed Feredj: Issues of Hierarchical Heterogeneous
Modeling in Component Reusability
Cecile Hardebolle, Frederic Boulanger, Dominique Marcadet, Guy Vidal-Naquet: A
Generic Execution Framework for Models of Computation
Hua-Jun Zeng, Zheng Chen, Wei-Ying Ma: A Unified Framework for Clustering
Heterogeneous Web Objects
− 26 −

Applying Rough Set Theory to Constructing
Knowledge Base for Critical Military
Commodity Management
Hua-Kai Chiou, 1 Yong-Ting Huang 2 and Gia-Shie Liu 3
1 Department of International Business, China Institute of Technology. 245 Sec.3,
Academia Rd., Nangang Taipei 11581, Taiwan. hkchiou@cc.chit.edu.tw
2 Graduate School of Resources Management and Decision Science,National
Defense University. 70, Sec.2, Central North Rd., Beitou Taipei 11258 Taiwan.
coby777.tw@yahoo.com.tw
3 Department of Information Management,Lunghwa University of Science and
Technology. 300, Sec.1, Wanshou Rd., Gueishan Shiang, Taoyuan County 33306,
Taiwan. liugtw@yahoo.com.tw
Abstract. Reduction of pattern dimensionality via feature extraction and feature
selection belongs to the most fundamental steps in data preprocessing. Feature selection
is a valuable technique in data analysis for information-preserving data reduction.
Features constituting the objects pattern may be irrelevant or relevant. A large
number of methods like discriminant analysis, logit analysis, recursive partitioning
algorithm, etc., have been used in the past for the prediction issues by pattern recognition.
These traditional approaches suffer from some limitations, often due to the
unrealistic assumption of statistical hypotheses or due to a confusing language of
communication with the decision makers. In this paper, we present applications of
rough set methods for feature selection in pattern recognition. Firstly, we employ
Delphi process to generate 39 critical attributes with respect to 6 key factors for
evaluation. We then utilize the rough set approach to discover a set of rules able to
discriminate among considered attributes for critical military commodity management.
There 10 reduct sets and 226 rules were derived from our proposed model,
we also stated some concluding remarks in the final section. Through this research
we found that rough set theory is an efficiency technique for pattern recognition to
solving decision making problems in real world.
Key words: Pattern Recognition, Rough Set Theory, Delphi Process, Discriminant
Analysis
− 27 −

Correspondence Analysis for Exploring the
Implementation of One Village One Product
Programs in Taiwan
Hua-Kai Chiou, 1 Benjamin J.C. Yuan 2 and Yen-Wen Wang 2,3
1 Department of International Business, China Institute of Technology. 245 Sec.3,
Academia Rd., Nangang Taipei 11581, Taiwan. hkchiou@cc.chit.edu.tw
2 Institute of Management of Technology, National Chiao Tung University. 1001,
Ta-Hsueh Rd., Hsinchu 30010, Taiwan. benjamin@cc.nctu.edu.tw
3 Industrial Economics & Knowledge Center,Industrial Technology Research
Institute. 195 Sec.4, Chung Hsing Rd., Chutung Hsinchu 31040, Taiwan.
stevenwang@itri.org.tw
Abstract. One Village One Product programs (OVOP) is a community-centered,
and demand-driven local economic development approach initiated by Oita Prefecture
in Japan in the 1970s. The uniqueness of the approach is that they intended
to achieve their regional economic development through adding value to products
using locally available resources through processing, quality control and marketing.
The objectives of OVOP programs in Taiwan are supported by government to promoting
the economic development and cooperative relationship of each country and
region through the localization and innovation. Firstly, we employ Delphi process to
converge 18 critical factors with respect to 3 key dimensions for evaluation. We then
utilize correspondence analysis to exploring the implementation of OVOP programs
and conduct some meaningful suggestions of the policy direction from these empirical
cases. Through this study we successfully demonstrate that correspondence
analysis is an efficiency technique for industrial analysis and strategy management
in real world.
Key words: OVOP, Correspondence Analysis, Delphi Process, Industrial Analysis
− 28 −

Extending Multivariate Planing
Mario Cortina–Borja 1
Centre for Paediatric Epidemiology and Biostatistics
Institute of Child Health, University College London
30 Guilford Street, London WC1N 1EH, UK
M.Cortina@ich.ucl.ac.uk
Abstract. Friedman and Rafsky (1981) introduced planing, a visualization technique
based on a triangulation procedure for constructing 2–dimensional representations
from a set of n multivariate observations based on preserving exactly relatively
few distances from the original distance matrix and plotting the observations on the
plane. The way these distances are selected and the order in which the observations
are positioned into the plane are induced by a minimal spanning tree (MST ) of the
data.
Other spanning trees could be used to provide the set of distances to be preserved
exactly in a 2–dimensional configuration. One is the exodic tree (ET ) (Gilbert,
1965), which is a not quite minimal spanning tree, though it may be regarded as a
close approximation to the MST (Cortina–Borja and Robinson, 2000). To construct
an ET we choose any point of the dataset as the root and label it as P0; we then
label the rest of the n points as {P1, P2, · · · , Pn−1}, the indices being assigned to
order the points according to their increasing distance from P0. Next, we link any
point Pi to the point Pj chosen from {P2, P3, · · · , Pi−1} in order to minimize its
distance with Pi, (i ≥ 1).
This paper extends two aspects of planing: first, obtaining 3–dimensional configurations;
second, using the ET as the structure defining the distances to be preserved
in the low–dimensional representation.
Key words: Exodic Tree, Minimal Spanning Tree, Planing, Visualization
References
Cortina–Borja, M. and Robinson, T. (2000) Estimating the Asymptotic Constants of
the Total Length of the Euclidean Minimal Spanning Tree with Power–Weighted
Edges. Statistics and Probability Letters, 47, 125–128.
Friedman, J.H. and Rafsky, L.C. (1991) Graphics for the Multivariate Two–Sample
Problem. Journal of the American Statistical Association, 76, 277–287.
Gilbert, E.N. (1965) Random Minimal Trees. SIAM Journal of Applied Mathematics,
13, 376–387.
− 29 −

Principal Axis Analysis with HDLSS bonuses!
Frank Critchley 1 , Ana Pires 2 , and Conceição Amado 2
1 Open University, UK
F.Critchley@open.ac.uk
2 IST, Lisbon
Abstract. Principal axis analysis rotates standardised principal components to optimally
detect subgroup structure, rotation being based on preferred directions in
the spherised data. As such, it is a computationally efficient method of exploratory
data analysis, particularly well-suited to detecting mixtures of elliptically contoured
distributions. High dimensional, low sample size (HDLSS) data are also discussed.
Overall, principal axis analysis exemplifies the maxim: two decompositions are better
than one. More technically, it is an example of invariant coordinate selection
(ICS).
− 30 −

Augmenting Model-Based Clustering with
Generalized Linkage methods
Nema Dean 1 and Rebecca Nugent 2
1 Department of Statistics, University of Glasgow, 15 University Gardens,
Glasgow G12 8QW, UK. nema@stats.gla.ac.uk
2 Department of Statistics, Carnegie Mellon University, Baker Hall, Pittsburgh,
PA 15213, USA. rnugent@stat.cmu.edu
Abstract. The fundamental assumption made by model-based clustering (Fraley
and Raftery 1998) is that the groups or sub-populations underlying the data have
(multivariate) Gaussian distributions, giving the overall population a finite mixture
model distribution. An assumption additionally made, is that the number and type
of components found to best fit the data are a good estimate of the number and type
of true groups in the data. Given the shape assumptions implicit in the choice of
Gaussian distributions - elliptical, symmetric contours - in cases of skewed, curved
or more generally complex-shaped groups, the equivalence of the mixture model
components and the underlying groups is likely false.
Since general continuous densities can be modelled arbitrarily well by mixtures
of Gaussian densities, the mixture model chosen may still be a good estimate of
the density of the data but it is likely that more than one component is identified
with each group. Generalized single linkage methods (Stuetzle and Nugent 2007)
use density estimates to create density-based similarity (or dissimilarity) measures
which can then be used as a replacement for Euclidean (or other types of) distance
in hierarchical agglomerative methods. Using the resulting model-based clustering
density estimate we can use the resulting dendrogram to visualize the hierarchical
structure of the components of the mixture model and make decisions about
combining components to estimate groups. Since it is difficult to easily summarize
information about complex shaped groups, offering a summary that is essentially
a subset of components of the original mixture model with means and covariance
matrices is an attractive alternative.
Key words: Model-Based Clustering, Generalized Single Linkage Clustering
References
Fraley, C. and Raftery, A. E. (1998): How many clusters? which clustering method?
- answers via model-based cluster analysis. The Computer Journal, 41, 578–588.
Stuetzle, W. and Nugent, R. (2007): A generalized single linkage method for estimating
the cluster tree of a density. Technical Report 514, Department of Statistics,
Univeristy of Washington.
− 31 −

Statistical analysis of human body movement
and group interactions in response to music
Frank Desmet, Marc Leman and Micheline Lesaffre
IPEM, Department of Musicology, Ghent University, Belgium fm.desmet@ugent.be
Abstract. The quantification of time series that relate to physiological data is a
challenging research topic for music research. Up to now, most studies have focused
on time dependent responses of individual subjects. However, little is known about
time dependent responses of between-subject interactions. At IPEM, Ghent University,
a large scale multidisciplinary research project targets the development of
innovative music interaction based on the movement of groups of subjects. Based on
a recent pilot experiment, we report new findings concerning the statistical analysis
of group synchronicity in response to musical stimuli. The aim was to refine future
experimental designs and to generate statistical pathways as practical guidelines for
researchers. The experiment was carried out in the context of the ACCENTA 2007
Fair in Ghent. 16 groups of 4 subjects took part in an experiment where they had
to move a wireless wii sensor in response to music. In the first condition, the subjects
were blind folded, while in the second condition, the subjects could see each
other. The movements of the subjects were recorded as acceleration data on PC.
Fourier coeffcients, total intensity, intra-group correlations and sampling entropy
characteristics were derived from these raw acceleration data. Combined with pre
and post survey data of the participants, we generated a multivariate dataset for
analysis. The statistical methods used in this study are basic descriptive statistics,
paired correlation analysis, auto correlation analysis, regression analysis and GLM
(general linear modeling). The different empirical methodologies were validated as
potential tools for the study of social embodied music interaction. It was found that
the synchronicity of the human-human interactions increases significantly in the
social context. The type of music is the predominant factor for the human-music
interaction in both the individual and the social context.
Key words: Human movement, Social Interaction, Statistical Analysis, music
− 32 −

Mixture Hidden Markov Models in Finance
Research
José G. Dias 1 , Jeroen K. Vermunt 2 and Sofia Ramos 3
1
Department of Quantitative Methods, ISCTE – Higher Institute of Social
Sciences and Business Studies, Edifício ISCTE, Av. das Forças Armadas,
1649–026 Lisboa, Portugal
jose.dias@iscte.pt
2
Department of Methodology and Statistics, Tilburg University, P.O. Box 90153,
5000 LE Tilburg, The Netherlands,
J.K.Vermunt@uvt.nl
3
Department of Finance, ISCTE – Higher Institute of Social Sciences and
Business Studies, Edifício ISCTE, Av. das Forças Armadas, 1649–026 Lisboa,
Portugal
sofia.ramos@iscte.pt
Abstract. Latent class or finite mixture modeling has proven to be a powerful
tool for analyzing unobserved heterogeneity in a wide range of applications (see, for
example, McLachlan and Peel (2000) or Dias and Vermunt (2007)). We introduce
in finance research the Mixture Hidden Markov Model (HHMM) that takes into
account both time-constant unobserved heterogeneity between and hidden regimes
within time series. This approach is flexible in the sense that it can deal with the
specific features of financial time series data, such as asymmetry, kurtosis, and unobserved
heterogeneity, aspects that are almost always ignored in finance research.
This methodology is applied to model simultaneously 12 time series of the returns of
Asian stock markets. Because we selected a heterogeneous sample of countries including
both developed and emerging countries, we expect that heterogeneity in market
returns due to country idiosyncrasies will show up in the results. The best fitting
model was the one with two latent classes or clusters at country level, which clearly
present different dynamics in the switching dynamics between the two regimes.
Key words: latent class model, finite mixture model, hidden Markov model, modelbased
clustering, stock indexes
References
Dias, J.G., Vermunt, J.K. (2007): Latent class modeling of website users’ search
patterns: Implications for online market segmentation. Journal of Retailing and
Consumer Services, 14(6), 359–368.
McLachlan, G.J., Peel, D. (2000): Finite Mixture Models. John Wiley & Sons, New
York.
− 33 −

Mapping Findspots of Roman Military
Brickstamps in Mogontiacum (Mainz)
and Archaeometrical Analysis
Jens Dolata 1 , Hans-Joachim Mucha 2 , and Hans-Georg Bartel 3
1 Generaldirektion Kulturelles Erbe Rheinland-Pfalz, Direktion Archäologie
Mainz, Große Langgasse 29, D-55116 Mainz, Germany,
dolata@ziegelforschung.de
2 Weierstraß-Institut für Angewandte Analysis und Stochastik (WIAS),
D-10117 Berlin, Germany, mucha@wias-berlin.de
3 Institut für Chemie, Humboldt-Universität zu Berlin, Brook-Taylor-Straße 2,
D-12489 Berlin, Germany, hg.bartel@yahoo.de
Abstract. 1775 Roman military brickstamps concerning to the 1st century A.D.
have been found in archaeological excavations in Mainz, the ancient Mogontiacum.
Making a catalogue of these ones for a paper on Roman military archaeology the
stamps have been classified and new types of stamps have been defined. All in all, 238
findspots of bricks and tiles of the 1st century have been investigated. Additionally,
the findspots are described by survey-coordinates. The mapping of the brickstamps
visualizes the size of the ancient city and gives details for the localization of military
camps and of civil settlement. Dating the brickstamps by epigraphical investigation
or by assigning them to a military brickyard based on geochemical analysis allows
the mapping of different periods. Two main maps have been plotted: (A) The earliest
brickstamps found in Mainz are from the period of Emperors Claudius and Nero (41 -
68 A.D., n = 932). They were manufactured by soldiers of legiones XXII Primigenia
and IIII Macedonica. (B) Brickstamps of legiones I Adiutrix, XIV Gemina, VII
Gemina, and XXI Rapax belong to the Flavian period (69 - 96 A.D., n = 843).
These two main maps can be compared with some maps showing a selection
of brickstamps from 3rd and 4th centuries (Emperors Caracalla, Constantine I or
Julian, and Valentinian I, n = 102). Thus the maps show a total of 1877 brickstamps
from 246 sites. In this paper we try to improve the situation of evaluating all this
maps for history of settlement and urban development. Using statistical methods
we compare the different entries of the maps. Every single entry is not of the same
weight according to the radius of the finding area. These different weights can be
taken into account in statistical analysis, for instance, in nonparametric density
estimation. The intention is to get a mapping of densities of brickstamps for different
urban regions. In combination of mapping and looking for dated brickstamps there
is a good chance to achieve new sources for the ancient history of Mogontiacum.
Key words: Roman bricks, archaeometry, nonparametric density, mapping
− 34 −

Multimodal Performance Analysis of
Electronic Sitar
Arne Eigenfeldt 1 and Ajay Kapur 2
1 Simon Fraser University
Burnaby, BC, Canada
2 California Institute of the Arts
Valencia, CA, USA
Abstract. This paper describes a custom-build system which extracts high-level
musical information from real-time sensor data received from an Esitar [1]. Data is
collected from sensors during rehearsal using one program, GATHER, and, combined
with audio analysis, is used to derive statistical coefficients which are used to identify
three different playing styles of North Indian music: Alap, Ghat, and Jhala. A realtime
program, LISTEN, uses these coefficients in a multi-agent analysis to determine
the current playing style.
The ESitar is an instrument which gathers gesture data from a performing artist
using sensors embedded on the traditional instrument. A number of different performance
parameters are captured including fret detection (based on position of left
hand on the neck of the instrument), thumb pressure (based on right hand strumming),
and 3-dimensions of neck acceleration. Audio features from the instrument
are computed as well, and include Root Mean Square (rms), spectral centroid, spectral
flux, spectral rolloff at 85
Analysis is done on sample data to derive statistical information (minimum,
maximum, mean, standard deviation) - for each sensor and audio analysis to derive
unique class coefficients. These coefficients are compared to incoming performance
data, which is also statistically analysed, by an multi-agent system.
Interactive and real-time computer music is becoming more complex, and the
requirements placed upon the software is equally increasing. Composers, hoping to
derive more understanding about a performer’s actions, are looking not just at incoming
audio, but also to sensor data, for such information. Our research has focused
upon a high-level musical cognition - playing style - rather than detail recognition
- beat or pitch tracking, for example - by augmenting real-time audio analysis with
sensor data analysis.
References
[1] Kapur, A., Tindale, A, Benning, M. S. and P. Driessen. ”The KiOm: A Paradigm
for Collaborative Controller Design”, Proceedings of the International Computer
Music Conference, New Orleans, USA, 2006.
− 35 −

Data compression and regression based on
local principal curves
Jochen Einbeck 1 and Ludger Evers 2
1 Department of Mathematical Sciences, Durham University, Durham, UK
2 School of Mathematics, University of Bristol, Bristol, UK
Abstract. Frequently the predictor space of a multivariate regression problem of
the type y = f(x1, . . . , xp) + ɛ is in fact much lower-dimensional than p, often even
(approximately) one-dimensional. Usual modeling attempts such as the additive
model y = f1(x1) + . . . + fp(xp) + ɛ, which try to reduce the complexity of the
regression problem by making additional structural assumptions, are then inefficient
as they ignore the inherent structure of the predictor space and involve complicated
model and variable selection stages.
In a fundamentally different approach, one may consider first approximating
the predictor space by a (usually nonlinear) curve passing through it, and then
regressing the response only against the one-dimensional projections onto this curve.
This entails the reduction from a p− to a one-dimensional regression problem.
As a tool for the compression of the predictor space we apply local principal
curves [1], which form a more flexible alternative to earlier proposed principal curve
algorithms [2] as they also allow for branched or disconnected curves. Taking things
on from the results presented in [1], we show how local principal curves can be
parameterized and how the projections are obtained. The regression step can then
be carried out using any nonparametric smoother. We illustrate the technique using
16- and higher dimensional data from astrophysical applications. Possible extensions
to more than one-dimensional nonparametric summaries of the predictor space are
discussed.
References
[1] EINBECK, J., TUTZ, G., and EVERS, L. (2005). Exploring multivariate data
structures with local principal curves. In: Weihs, C. and Gaul, W. (Eds.): Classification
- The Ubiquitous Challenge. Springer, Heidelberg, pages 256-263.
[2] CHANG, K. and GHOSH, J. (1998). Principal curves for nonlinear feature extraction
and classification. SPIE Applications of Artificial Neural Networks in
Image Processing III, 3307, 120–129.
− 36 −

Regression-autoregression based clustering
Igor Enyukov
StatPoint Ltd., Moscow
Abstract. The usual approach to the clustering of cases into k groups (for example,
k-means ) can be regarded as a regression of the source variables on a set of k dummy
binary (indicator) variables which satisfy the following conditions:
• for i-th case only one of these variables has value 1
• if j-th of such a variable has value 1 for i-th case, it means that the case belongs
to j-th group.
These dummy variables in their turn are some nonlinear functions of the source
variables and their values are being evaluated by performing a classification procedure.
The set of dependent variables and predictors is the same in this approach.
So it may be regarded as an autoregression approach. Such approach may lead to
problems when we work with spatial distributed data (like objects with geographical
coordinates) because the objects that are close by physical properties (for example,
by seasonal properties of river stocks) may be situated rather far in the geographical
space. In this paper is suggested to use a smoothed variant of such indicator
nonlinear functions. For this aim are used the radial basis functions (RBF), such
that their centers are the group centers (in the end of work of the procedure). In
this case set of dependent variables (source variables which we want to explain by
clusterization) may be distinct from the set of explaining (independent variables or
predictors) variables. For example, the last ones may be geographical coordinates.
Such approach may be regarded as regression-autoregression. The regression problem
is lnear relatively RBF. This approach gives a possibility to define the class
centers and to evaluate their number as well. For this purpose a kind of ”step-wise”
regression algorithm can be used.
− 37 −

Realoptionen bei der Bewertung von neuen
Produkten
Said Esber and Daniel Baier
Lehrstuhl für Marketing und Innovationsmanagement, Erich-Weinert-Str. 1,
D-03046 Cottbus
saidesber1@yahoo.com, baier@tu-cottbus.de
Abstract. Bei der Entwicklung von neuen Produkten ist es für das F&E-Management
sehr wichtig, technische, marktbedingte und wettbewerbsbedingte Unsicherheiten
zu berücksichtigen. Aus den zunehmenden Markt- und Umfeldänderungen resultiert,
dass Investitionsentscheidungen verstärkt unter hoher Unsicherheit getroffen
werden müssen. Die Realoptionsbewertung ermöglicht hier ein besseres Verständnis
für Unsicherheiten in F&E-Projekte, die Flexibilität des Managements
während des Projektlebenslaufs und die Auswahl der besten Projektalternative.
Der vorliegende Beitrag beschreibt die Anwendung des Realoptionsansatzes im Bereich
der Informationstechnologie (IT) durch das Management der Produktentwicklung
bei einem Videokonferenz-System. Zunächst wird ein Überblick über den Realoptionsansatz,
die Eigenschaften verschiedener Typen von Realoptionen und ihr
Zusammenhang mit anderen verwendbaren zusätzlichen oder alternativen Bewertungsmethoden
von F&E-Projekten (Sensitivitätsanalyse, Szenarioanalyse, Monte-
Carlo-Stimulation und Entscheidungsbäume) gegeben. Anschließend wird ein entscheidungsunterstützendes
excelbasiertes Tool zur Berechnung der Realoptionen,
zur Entwicklung der Entscheidungsbäume und zur Durchführung der Monte-Carlo-
Simulationen verwendet. Schließlich wird die Entscheidung, dass Videokonferenz-
System (BRAVIS) im Produktionsprozess in deutschen VW-Unternehmen einzuführen,
als Anwendungsbeispiel analysiert.
Key words: Realoptionen, Bewertungsverfahren von Optionen, Entscheidungen
unter Unsicherheit, IT-Projekte, Verfahren der Risikoanalyse
References
Dixit, A. K. and Pindyck, R. S. (1995): The Options Approach to Capital Investment.
Harvard Business Review, Mai/Juni, 105–115.
Pritsch, G. (2000): Realoptionen als Controlling-Instrument - Das Beispiel pharmazeutische
Forschung und Entwicklung. Gabler, Wiesbaden.
Rese, A. and Baier, D. (2007): Deciding on new products using a computer-assisted
real options approach. Int. J. of Techn. Intelligence & Planning, 3(3), 292–303.
− 38 −

Regression and Classification using Bayesian
Additive Voronoi Tessellation Models
Ludger Evers 1
School of Mathematics, University of Bristol, Bristol, UK, l.evers@bris.ac.uk
Abstract. Voronoi-tessellation-based regression and classification models are based
on approximating the unknown regression function by a discontinuous, piecewise
constant (or linear) function. The discontinuities are modeled by a Voronoi tessellation
of the covariate space. This distinguishes them from recursive partitioning
models like CART which model the discontinuities by (typically axis aligned) hierarchical
splits. Voronoi-tessellation-based regression and classification models are
typically considered in a Bayesian framework, where inference is done using Reversible
Jump MCMC techniques.
These methods however possess two important drawbacks. In many situations
only a small proportion of the covariates studied are relevant to the regression or
classification task at hand. The pairwise distances, which the Voronoi tessellation
is based on, are then dominated by the irrelevant covariates, i.e. it is increasingly
difficult to find a Voronoi tessellation that is informative for the problem at hand.
Second, the estimated regression function is, due to its high-dimensional and complex
nature, typically difficult to interpret.
We propose to use an additive model of the form P
I fI(xI) that addresses these
two shortcomings. Each fI(·) is based on a Voronoi tessellation of a subspace of the
covariates. A hierarchical model is used for the inclusion of the covariates in order
to ensure that the model makes sparing use of the covariates. In many situations,
each of the fI(·) involves only a small number of covariates and thus allows for easy
interpretation. A further benefit of this approach is that it allows for constructing
faster mixing MCMC compared to the basic non-additive model.
A case study and an empirical comparison with competing methods like CART,
BART, MARS, and Support Vector Machines, is presented both for regression and
classification tasks.
Keywords
Additive model, random basis, transdimensional simulation.
− 39 −

Ross-linguistic regularities in the monosyllabic
system
Gertraud Fenk-Oczlon and August Fenk
Alps-Adriatic University of Klagenfurt
Abstract. We assumed cross-linguistic correlations between the number of monosyllabic
words (a), of syllable types (b), of phonemes per syllable(c), and of the size
of the phonemic inventory (d). Menzeraths (1954:112-121) descriptions of 8 Indo-
European languages and Campbells (1991) data regarding their phonemic inventory
offered the basis for a statistical evaluation. All correlations between a, b, and c
turned out to be significant, those between these three parameters and d almost
significant (Fenk-Oczlon & Fenk, to appear). The discussion of these results within
a systems-theoretical framework includes: (I) Diachronic changes: A comparison of
the Beowulf Prologue in Old English (OE) with its translation into Modern English
(ME) shows a remarkable increase of monosyllables from 105 in OE to 312 in ME
and a concomitant increase of the mean syllable complexity from 2.63 phonemes
in OE to 2.88 in ME. (II) Semantic functions: The verb as well as the adverb or
preposition forming a phrasal verb are often polysemous. In a short analysis of a collection
of 1406 English phrasal verbs we found that 1367 or 97 % of the verbs that
were part of the phrasal verb construction were monosyllabic. (39 phrasal verbs
included a bisyllabic verb and only one was found with a trisyllabic verb.) (III)
General relations between a languages phonemic inventory, the number of cogitable
combinatorial possibilities and the number of those phonotactic possibilities actually
realized in the monosyllables of typologically different languages.
References
Campbell, G. L. 1991. Compendium of the worlds languages. London: Routledge.
Fenk-Oczlon, G. & Fenk, A. (to appear). Complexity trade-offs between the subsystems
of language, In M. Miestamo, K. Sinnemaeki & F. Karlsson, (eds.) Language
Complexity: Typology, Contact, Change, pp. 43-65. Amsterdam: John
Benjamins.
Menzerath, P. (1954). Die Architektonik des deutschen Wortschatzes. Hannover/Stuttgart:
Duemmler.
− 40 −

Validity of images from binary coding tables.
Student motivation surveys: some evidence
K. Fernández-Aguirre 1 and M. A. Garín-Martín 1
University of the Basque Country (UPV/EHU), Bilbao, Spain
karmele.fernandez@ehu.es
Abstract. Using both artificial and real data, this paper analyses the superiority
of Correspondence Analysis (CA) over Principal Component Analysis (PCA) as a
procedure for displaying and exploring data in the processing of contingency tables
or binary tables.
Simple and Multiple Correspondence Analysis (CA and MCA) are becoming
more and more widely used in many areas of science. However, PCA is much better
known and is accessible in software packages, so it continues to be used even at the
risk of obtaining poor results when it is not applied to quantitative data. A second
point examined is the obtaining of low percentages of projected variance on the first
factorial axes in CA applications. This may deter users from applying these methods,
as it suggests that the visualization obtained will be of poor quality. This problem
has been widely discussed: (Benzécri, 1979), (Greenacre, 1994) propose corrections
in projected variance rates. Moreover, (Lebart, 1984, 1998, 2000) consider the case
of a matrix associated with a symmetric graph and analytically study the variations
in representation depending on the different codifications of the associated matrix.
On the one hand, our study shows the superiority of CA for the reconstitution
and visualization of an M matrix associated with a G symmetric graph over the
visualization obtained with PCA. On the other hand, we present four examples which
show that projected variance rates are a highly pessimistic measure of the quality
of a representation. These examples of application to survey data examine various
aspects of the motivation of university students in the field of education, and in
particular present extremely low percentages of projected variance on the first axes.
The application of MCA enables us to obtain apparently fragile but interpretable
images. Moreover, classification analysis provides various types of student with clear
interpretations, leading to robust conclusions.
Key words: Binary tables, Visualization, Correspondence Analysis, Clustering
References
Lebart, L., Morineau, A. and Warwich, K. M. (1984): Multivariate Descriptive Statistical
Analysis. John Wiley & sons, New York.
− 41 −

A Statistical Theory of Musical Consonance
Proved in Praxis
Jobst Fricke
Universität zu Köln
Abstract. One of the recent models of consonance perception of musical intervals
is based on the simulation of neural autocorrelation. It is assumed that the shape of
the coinciding neural spikes resembles Dirac-delta-impulses. Then, consonant musical
intervals are recognized as consonant, if and only if the frequencies of the interval
tones exactly form a simple numerical proportion. But intervals are perceived to be
consonant too when they have a slight displacement of the simple numerical proportions.
The models of Tramo et al. (2001) and Ebeling (2007) are for the first time
in accordance with the reality of perception by introducing larger impulses. Both of
them just realize the autocorrelation of interval tones that consist of impulses with a
width different from zero. In fact, the time window for the spikes’ coincidence has a
width different from zero. This is the latency which is relevant in cognitive processes.
In order to adapt the model to reality, the width of the statistical distribution of
neural impulses should be considered.
It is investigated to what extent the behavior of the model corresponds to the auditory
perception in a realistic environment. The experimental data were taken from
a study dealing with the judgment of intervals in a musical context (Fricke 2005b).
Keywords
MUSIC, CONSONANCE THEORY
References
EBELING, M. (2007): Verschmelzung und neuronale Autokorrelation als Grundlage
einer Konsonanztheorie. Frankfurt/M. u.a..
FRICKE, J. (2005b): Classification of Perceived Musical Intervals, in: Claus
Weihs u. Wolfgang Gaul (Hrsg.): Classification - The Ubiquitous Challenge,
Berlin/Heidelberg/New York: Springer, S. 585–592.
TRAMO, M., CARIANI, P., DELGUTTE, B. and BRAIDA, L. (2001): Neurobiological
Foundations for the Theory of Harmony in Western Tonal Music, in: R.
Zatorre and I. Peretz (Hrsg.): Biological Foundations of Music, Annals of the
New York Academy of Sciences, Vol. 930.
− 42 −

An Improved Criterion for Clustering Based
on the Posterior Similarity Matrix
Arno Fritsch and Katja Ickstadt
Technische Universität Dortmund, Fakultät Statistik
Vogelpothsweg 87, 44221 Dortmund
arno.fritsch@tu-dortmund.de, ickstadt@statistik.uni-dortmund.de
Abstract. Complex Bayesian cluster models are often fitted using Markov Chain
Monte Carlo (MCMC) algorithms. A problem is then how to summarize the MCMC
sample c (1) , . . . , c (M) from the posterior distribution of clusterings p(c|y) with a single
estimated clustering ĉ. The problem is complicated by the fact that the labels
associated with the clusters can switch during the MCMC run. One way to overcome
this is to derive the estimate ĉ based on the posterior similarity matrix, a matrix
with entries πij = P (ci = cj|y), the posterior probabilities that the observations i
and j are in the same cluster. This approach is taken for example in the Bayesian
cluster models for gene expression microarray data by Medvedovic et al. (2004) and
Dahl (2006). The former applies hierarchical clustering to the matrix of (1 − πij),
while the latter tries to minimize a loss function proposed by Binder (1978). We
show that this minimization is equivalent to maximizing the Rand index between
estimated and true clustering and propose a new criterion for choosing ĉ, the posterior
expected adjusted Rand index with the true clustering. In a simulation study
with a Dirichlet process mixture model it is shown that our new criterion leads to
estimated clusterings closer to the true one than the other two approaches and that
it also outperforms the usage of the maximum a posteriori (MAP) clustering.
Key words: Adjusted Rand index, Bayesian cluster analysis, Markov Chain Monte
Carlo, Loss functions
References
Binder, D.A. (1978): Bayesian Cluster Analysis. Biometrika, 65, 31–38.
Dahl, D.B. (2006): Model-based Clustering for Expression Data via a Dirichlet Process
Mixture Model. In: K.A. Do, P. Müller and M. Vannucci (Eds.): Bayesian
Inference for Gene Expression and Proteomics, Cambridge University Press,
New York, 201–216.
Medvedovic, M., Yeung, K. and Bumgarner, R. (2004): Bayesian Mixture Model
Based Clustering of Replicated Microarray Data. Bioinformatics, 20, 1222–
1232.
− 43 −

On the Use of Student Samples in Major
Marketing Research Journals. A Meta-Study
Sebastian Fuchs and Marko Sarstedt
Institute for Market-based Management, Munich School of Management, D-80539
Munich, Germany imm@bwl.lmu.de
Abstract. In the last years, almost every marketing research journal has experienced
a big leap in the number of high-quality paper submissions. This led to
an increased competition among contributing authors and heightened requirements
for manuscript submissions. In this context, the manuscript evaluation criteria for
almost every marketing journal underline the importance of the sample’s characteristics
and how well it represents the population being studied. However, the predominance
of student samples in empirical marketing research documents the divergence
between these theoretical requirements and practical implementation. Despite theoretical
and empirical objections, several authors claim that a silent acceptance of
the usage of student samples has become observable, even in top research societies.
According to Peterson (2001), this development is problematic as generalizations are
only feasible if replicating research with non-student subjects is carried out. Thus,
the objective of this paper is to analyze the development of the usage of student
samples in the most reputable marketing research journals. For this purpose, all
eleven marketing journals rated A or A+ in the ranking, developed on behalf of
the Association of University Professors of Management in German-speaking countries
(VHB) were investigated. A total number of 1.491 studies that appeared since
2005 were analyzed with regard to samples used, measures evaluated and limitations
addressed. The results show vast differences between the various journals.
Key words: Student Samples, Sampling, Marketing Research
References
Peterson, R.A. (2001): On the Use of College Students in Social Science Research:
Insights from a Second-Order Meta-analysis Journal of Consumer Research, 28
(3), 450–461.
Völckner, F. and Sattler, H. (2007): Empirical Generalizability of Consumer Evaluations
of Brand Extensions International Journal of Research in Marketing, 24
(2), 149–162.
− 44 −

Multi-Dimensional Scaling applied to
Hierarchical Fuzzy Rule Systems
Thomas R. Gabriel, Kilian Thiel, and Michael R. Berthold
Chair for Bioinformatics and Information Mining
Department of Computer and Information Science
University of Konstanz, Box 712, 78457 Konstanz, Germany
{gabriel|thiel|berthold}@inf.uni-konstanz.de
Abstract. This paper presents an approach for visualizing high-dimensional fuzzy
rules arranged in a hierarchy together with the training patterns they cover. A standard
multi-dimensional scaling method is used to map the rule centers of the top
hierarchy level to one coherent picture. Rules of the underlying levels are projected
relatively to their superior level(s). In addition to the rules, all patterns are mapped
onto the two dimensional projection in relation to the positions of the corresponding
rule centers. The visualization is further extended by showing hierarchical relationships
between overlapping rules of different levels as generated by a hierarchical rule
learner. This delivers interesting insights into the rule hierarchy and makes the model
more explorable. Additionally, rules can be highlighted interactively emphasizing the
subsequent rules at all underlying levels together with their covered patterns. We
demonstrate that this technique allows investigation of interesting rules at different
levels of granularity, which even makes this approach applicable to a large number
of rule sets. The proposed technique is illustrated and discussed on a number
of hierarchical rule model visualizations generated on well-known benchmark data
sets.
Key words: Multi-Dimensional Scaling, Fuzzy Rule Induction, Rule Hierarchy
References
Berthold, M.R., (2003): Mixed Fuzzy Rule Formation. International Journal of Approximate
Reasoning, 32:67–84.
Gabriel, T.R. and Berthold, M.R. (2003): Constructing Hierarchical Rule Systems.
In: M.R. Berthold, H.-J. Lenz, E. Bradley, R. Kruse, C. Borgelt (Eds.): Proc. 5th
International Symposium on Intelligent Data Analysis. Springer, Berlin, 76–87.
Gabriel, T.R. and Thiel, K. and Berthold, M.R. (2006): Rule Visualization based on
Multi-Dimensional Scaling. In: IEEE International Conference on Fuzzy Systems.
IEEE Press, Vancouver, 66–71.
− 45 −

Ein Leistungszentrum für die digitale
Unterstützung der Durchführung, Auswertung
und Veröffentlichung von archäologischen
Feldprojekten (Ausgrabung, Survey)
Ulrich-Walter Gans und Matthias Lang
Institut für Archäologische Wissenschaften der RUB, Fakultät für
Geschichtswissenschaft, E-Mail: johannes.bergemann@rub.de
Zusammenfassung. Seit etwa zwei Jahrzehnten werden die in der archäologischen
Feldforschung in sehr hohen Quantitäten anfallenden Informationen überwiegend
bis ausschließlich in digitaler Form erfasst. Zwar entstanden an zahlreichen Orten
umfangreiche Datenbanken, allerdings weisen diese extrem heterogene Strukturen
auf. Insellösungen ohne Verbindungen sind die Regel. Weitere grundsätzliche Probleme
betreffen die langfristige Erhaltung und Pflege vorhandener Datenpools sowie
die schnell wechselnden Betriebssysteme. Ein interdisziplinäres Team aus den
Bereichen Archäologie, Informationsmanagement, Softwareentwicklung und Geoinformatik
will Archäologen künftig einen völlig neuartigen Umgang mit den digitalen
Medien ermöglichen. Bislang ist es nur möglich, über das Internet die auf Servern
von Universitäten, Forschungsinstituten und Museen verteilten Datenbanken einzeln
abzufragen. ArcheoInf will einen Mediator erstellen, der fähig ist, in zahlreichen archäologischen
Feldforschungsdatenbanken gleichzeitig zu recherchieren, ohne dass
die Benutzer die Abfrageoberfläche wechseln müssen. Dem Mediator liegen ein möglichst
viele Bereiche der Archäologie umfassender Thesaurus und eine entsprechende
Ontologie zugrunde, die eine komfortable Suche in Feldforschungsdaten ermöglicht.
So wird ein dem Open-Access-Gedanken verpflichtetes Repositorium erstellt, in dem
kostenfrei auf archäologische Sach- und Bilddaten zugegriffen werden kann. Weiter
ist die Einbettung von bibliographischen Daten, Zitaten, Bestandsnachweisen und
elektronischen Volltexten vorgesehen. Gleichzeitig gilt es einen zentralen WebGis-
Server aufzubauen, der geoinformatische Anwendungen der Archäologen in Verbindung
zu Text- und Bilddaten setzt. An dem von der Deutschen Forschungsgemeinschaft
finanzierten Projekt arbeiten Archäologen der Ruhr-Universität Bochum, Informatiker
der Technischen Universität Dortmund, Geoinformatiker der Hochschule
Bochum sowie die Universitätsbibliotheken Bochum und Dortmund mit. Weitere
archäologische Projektpartner in Berlin, Bochum, Cottbus, Darmstadt, Karlsruhe,
Köln, Frankfurt und Tübingen sind assoziiert.
− 46 −

Scalable and Incrementally Updated Hybrid
Recommender Systems
Zeno Gantner and Lars Schmidt-Thieme
Information Systems and Machine Learning Lab (ISMLL)
University of Hildesheim, Germany
{gantner,schmidt-thieme}@ismll.uni-hildesheim.de
Abstract. A typical approach in collaborative filtering is to treat the ratings as a
matrix with many unknown entries, and to use the known data to approximate the
complete matrix, including the unknown ratings. George and Merugu demonstrated
that using a co-clustering algorithm to approximate the ratings matrix achieves prediction
accuracies comparable to techniques like SVD, NMF, or kNN, while having
computational properties which allow the use of the method in dynamic scenarios,
where incoming data has to be incorporated instantly. However, pure collaborative
filtering approaches suffer from insufficient data, especially in cases when there are
only a few users, or when there are new items which have not yet been rated.
To overcome this problem, we combine co-clustering with a Naïve Bayes classifier
to predict ratings based on both the ratings matrix and item attributes. The simplicity
of the classifier allows us to preserve the desirable properties (parallelization,
scalability, incremental updates). Our evaluation indicates that the hybrid recommender
system performs better than pure co-clustering.
Key words: hybrid recommender systems, collaborative filtering, content-based
filtering, co-clustering, naïve bayes
References
Banerjee, A., Dhillon, I., Ghosh, J., Merugu, S. and Modha, D. (2007): A Generalized
Maximum Entropy Approach to Bregman Co-clustering and Matrix
Approximation. The Journal of Machine Learning Research, 8, 1919–1986
Hauger, S., Tso, K. and Schmidt-Thieme, L. (2007): Comparison of Recommender
System Algorithms focusing on the New-Item and User-Bias Problem. In: The
31st Annual Conference of the German Classification Society on Data Analysis,
Machine Learning, and Applications.
George, T. and Merugu, S. (2005): A Scalable Collaborative Filtering Framework
Based on Co-Clustering. In: Proceedings of the 5th IEEE Conference on Data
Mining (ICDM). IEEE Computer Society, Los Alamitos, CA, USA, 625–628
− 47 −

Non-Gaussian nature of ENSO signals and
climate shifts: implications for regional
studies off the western coast of South America
Bernard Garel 1 , J. Boucharel, B. Dewitte and Y. du Penhoat 2
1 Institut de Mathmatiques de Toulouse (IMT-LSP)
bernard.garel@math.univ-toulouse.fr
2 Laboratoire d’Etudes en Gophysique et Ocanographie Spatiales (LEGOS)
julien.boucharel@legos.obs-mip.fr
Abstract. El Nio/Southern Oscillation (ENSO) exhibits a significant modulation
at decadal timescales which is also associated to changes of its characteristics (amplitude,
frequency, propagation, predictability). Among these characteristics, some
of them are generally ignored in ENSO regional studies, such as asymmetry (number
of warm and cold events is not equal) and deviation of its statistics from those
of an assumed Gaussian distribution. They tend to reduce ENSO prediction skill.
Empirical variance shifts (assumed to be an index of low frequency variability) first
detected in the western tropical Pacific, propagate (with propagation characteristics
related to the unstable modes of ENSO) and grow eastward along the equator,
leading to enhanced SST anomalies, asymmetry.
Statistical tests are used to quantify the non-Gaussian nature and asymmetry of
ENSO typical indices from in situ data and a variety of models (from intermediate
complexity models to full physics coupled general circulation models). It is tested
if ENSO can be accounted for by a non-Gaussian alpha-stable law (i.e. a more
heavy-tailed distribution than Gaussian), by a mixture of distributions or by a non
stationary process dominated by mean state and empirical variance abrupt changes.
This last issue is achieved by a shift detection method applied to ENSO typical
indices. Implications for the interpretation of proxies of the upwelling variability off
the coast of Peru are discussed.
Key words: alpha-stable distributions, mixtures, non-stationary process
− 48 −

Likelihood ratio test for general mixture
models
Elisabeth Gassiat 1
Université Paris-Sud 11, Bâtiment 425, 91405 Orsay Cédex, France
elisabeth.gassiat@math.u-psud.fr
Abstract. We investigate the likelihood ratio test (LRT) for testing hypotheses on
the mixing measure in mixture models with possibly structural parameter. The main
result gives the asymptotic distribution of the LRT statistics under some conditions
that are proved to be almost necessary. Asymptotic distribution of the LRT statistics
under contiguous alternatives may be derived. This applies to various testing
problems: the test of a single distribution against any mixture, with application to
Gaussian, Poisson and binomial distributions; the test of the number of populations
in a finite mixture with possibly structural parameter. This allows to prove that, for
the simple contamination model, the asymptotic local power under contiguous hypotheses
may be arbitrarily close to the asymptotic level when the set of parameters
is large enough.
Key words: Likelihood ratio test, mixture models, number of components, local
power, contiguity
References
Azais, J.-M., Gassiat, E. and Mercadier, C. (2006): Asymptotic distribution and
power of the likelihood ratio test for mixtures: bounded and unbounded case.
Bernoulli,12(5),775-799.
Azais, J.-M., Gassiat, E. and Mercadier, C. (2007): The likelihood ratio test for
general mixture models with possibly unknown structural parameters. ESAIM
P. and S.,submitted.
Gassiat, E. (2002): Likelihood ratio inequalities with applications to various mixtures.
Ann. Inst. H. Poincaré Probab. Statist., 6, 897–906.
− 49 −

On a Location of the Retail Units and
Equilibrium Price Determination.
Vladimir Gazda 1
Technical University in Kosice, Nemcovej Str., 040 01 Kosice, Slovakia
vladimir.gazda@tuke.sk
Abstract. The classical view on the price determination is based on perfect competition
assumption, which implies the only price existence accepted by all retailers.
Traditionally, we suppose that all consumers neglect the search costs spent by looking
for the most appropriate purchasing possibility. New views on the price - location
competition among the retailers were presented by Hotelling and, consequently,
by d’Aspremont et al.; Gabszewicz and Thisse; Dobson and Waterson; Martinez et
al. who stressed mainly a continual approach. The discrete model describing the relation
between the search costs and the selling price was described by Stigler (1961)
in his search theory. His approach is based on the sequential search of particular
retail places and is focused more on the searching process itself than the equilibrium
price determination. We propose a price equilibrium problem formulation in
the more complicated (not sequential) consumers’ and retailers’ structure.
The model describes the behaviour of the cost minimising homogenous consumers
and the retailers’ price policy. There, the union of the set of cosumers V1,
the set of retailers V2, a virtual source s and a virtual sink u gives a set V of digraph
nodes. Then, E = {s} × V1 ∪ V1 × V2 ∪ V2 × {u} is a set of digraph edges. We define
the unit cost function as c : E → N0 where cs,i = 0, ci,j = di,j, and cj,u = pj, where
di,j ∈ N is the search cost spent by the i-th consumer to reach the j-the retail
centre and pj ∈ πj is a price of the j-the retailer. It is proved that the min cost flow
in the digraph G = (V, E, c) models the optimal behaviour of all consumers. Then,
the normal form game of retailers S = (V2, Q Q
j πj, , j µj) describes the behaviour
of retailers with their payoff functions µj derived from the min-cost decisions of the
consumers. The Nash equilibrium price strategies of the retailers are discussed in
the article.
Key words: Graph, Game, Consumption, Retailer, Min-cost Flow, Nash Equilibrium
− 50 −

The Potential of Social Intelligence for
Collective Intelligence
Andreas Geyer-Schulz 1 and Bettina Hoser 2
1 Information Service and Electronic Markets andreas.geyer-schulz@kit.edu
2 bettina.hoser@kit.edu
Abstract. In this contribution we review the history and potential of social intelligence
for driving collective intelligence. Different social networks generated by
computer-mediated communication have been researched extensively in the past.
The development of new technologies and applications on the Internet has resulted
in a recent dramatic rise of user participation within these networks. We systematically
explore the possibility of cross-usage of information about social network
structures for personal, community or organisational services. In the other direction,
we investigate the potential of improving community services by integrating
information on the network structure with personal, organisational and mass information.
Key words: social networks, social network analysis, social intelligence, collective
intelligence
References
Hoser, B, and Geyer-Schulz, A. (2005): Eigenspectralanalysis of Hermitian Adjacency
Matrices for the Analysis of Group Substructures. Journal of Mathematical
Sociology, 29(4), 265–294.
Wassermann, S. and Faust K. (1994): Social Network Analysis, Methods and Applications.
Cambridge University Press, Cambridge.
− 51 −

Isolated vertices in random intersection graphs
Erhard Godehardt 1 , Jerzy Jaworski 2 and Katarzyna Rybarczyk 2
1 Clinic of Thoracic and Cardiovascular Surgery, Heinrich Heine University, 40225
Düsseldorf, Germany; godehard@uni-duesseldorf.de
2 Faculty of Mathematics and Computer Science, Adam Mickiewicz University,
60769 Poznań, Poland; jaworski@amu.edu.pl, kryba@amu.edu.pl
Abstract. In applications like the analysis of non-metric data, it is a natural approach
to classify objects according to the properties they possess. Often it is useful
to consider two objects similar if they share at least s properties (with a given arbitrary
number s). Then an effective model to analyze the structure of similarities
between objects is the random intersection graph G(m, n, P(m)), with a set of vertices
V generated by the random bipartite graph BG(m, n, P(m)) with bipartition (V, W),
where clusters are defined as given subgraphs of the generated intersection graph.
In BG(m, n, P(m)) the number of neighbors (properties) of a vertex v ∈ V (object) is
assigned according to the probability distribution P(m) and an edge between v ∈ V
and w ∈ W means that the object v has the property w. Moreover in G(m, n, P(m)),
an edge connects v1 and v2 (v1, v2 ∈ V) if and only if in BG(m, n, P(m)) they have
at least s common properties. The models were introduced in Godehardt and Jaworski
(2002). Using specific properties of such graphs, we can test the hypothesis
of randomness of the underlying data set.
Our main purpose is to study the number of isolated vertices (objects similar
to no other) in G(m, n, P(m)). Previous results concerning this problem considered
only the case, where each vertex had the same number of properties and s = 1. In
our new approach we manage to cope with dependencies between edge appearances
for s ≥ 1 (which is important from the application point of view). Moreover we give
results for the case, where the number of properties differs between objects (different
distributions P(m)). We give the asymptotics for the probability of nonexistence of
isolated vertices in G(m, n, P(m)) and conditions for asymptotic convergence of the
number of isolated vertices to the Poisson distribution.
Key words: Random Intersection Graphs, Isolated Vertices, Non-metric Data
Analysis
References
Godehardt, E. and Jaworski, J. (2002): Two Models of Random Intersection Graphs
for Classification In: M. Schwaiger and O. Opitz (Eds.): Exploratory Data Analysis
in Empirical Research. Springer, Berlin, 68–81.
− 52 −

A note on constrained EM algorithms
for mixtures of elliptical distributions
Francesca Greselin 1 and Salvatore Ingrassia 2
1 Dipartimento di Metodi Quantitativi per le Scienze Economiche e Aziendali,
Università di Milano Bicocca (Italy) francesca.greselin@unimib.it
2 Dipartimento di Economia e Metodi Quantitativi, Università di Catania (Italy)
s.ingrassia@unict.it
Abstract. We extend some theoretical results about the likelihood maximization
on constrained parameter spaces to mixtures of multivariate elliptical distributions.
In particular, mixtures of multivariate t distributions provide a robust parametric
extension to the fitting of data with respect to normal mixtures. In this framework,
the degrees of freedom can act as a robustness parameter, tuning the heaviness of
the tails, and down weighting the effect of the outliers on the parameters estimation.
Further, a constrained monotone algorithm implementing maximum likelihood mixture
decomposition of multivariate t distributions is proposed, to achieve improved
convergence capabilities and robustness. Numerical studies are presented in order
to demonstrate the better performance of the algorithm, comparing it to earlier
proposals.
Key words: Mixture models, Robust Clustering, EM algorithm, elliptical distributions,
t-distribution.
References
Hennig, C. (2004): Breakdown points for maximum likelihood estimators of locationscale
mixtures. The Annals of Statistics, 32, 1313–1340.
Hathaway, R.J. (1986): A constrained formulation of maximum-likelihood estimation
for normal mixture distributions. The Annals of Statistics, 13, 795–800.
Ingrassia, S. and Rocci, R. (2007): Constrained monotone EM algorithms for finite
mixture of multivariate Gaussians. Computational Statistics & Data Analysis,
51, 5339–5351.
McLachlan, G. J. and Peel, D. (2000): Finite Mixture Models, John Wiley & Sons,
New York.
− 53 −

Support Vector Machines in the Primal using
Majorization and Kernels
Patrick J.F. Groenen 1 , Georgi Nalbantov 2 , and Cor Bioch 3
1 Econometric Institute, Erasmus University Rotterdam,
Rotterdam, The Netherlands groenen@few.eur.nl
2 Econometric Institute, Erasmus University Rotterdam,
Rotterdam, The Netherlands and MICC, University Maastricht, Maastircht
nalbantov@few.eur.nl
3 Econometric Institute, Erasmus University Rotterdam,
Rotterdam, The Netherlands bioch@few.eur.nl
Abstract. Support vector machines have become one of the main stream methods
for two-group classification. At the 2006 GfKl meeting in Berlin, we proposed SVM-
Maj, a majorization algorithm that minimizes the SVM loss function (see, Groenen,
Nalbantov, and Bioch, 2007, 2008). A big advantage of majorization is that in each
iteration, the SVM-Maj algorithm is guaranteed to decrease the loss until the global
minimum is reached. Nonlinearity was reached by replacing the predictor variables
by their monotone spline bases and then doing a linear SVM. A disadvantage of
the method so far is that if the number of predictor variables m is large, SVM-Maj
becomes slow.
In this paper, we extend the SVM-Maj algorithm in the primal to handle efficiently
cases where the number of observations n is (much) smaller than m. We
show that the SVM-Maj algorithm can be adapted to handle this case of n ≫ m as
well. In addition, the use of kernels instead of splines for handling the nonlinearity
becomes also possible while still maintaining the guaranteed descent properties of
SVM-Maj.
Key words: Support vector machines, Iterative majorization, Binary classification
problem, Kernel
References
Groenen, P.J.F., Nalbantov, G., and Bioch, J.C. (2007): Nonlinear support vector
machines through iterative majorization and I-splines. In: R.Decker, H-.J. Lenz
(Eds.): Advances in data analysis. Springer, Berlin, 149–162.
Groenen, P.J.F., Nalbantov, G., and Bioch, J.C. (2008, in press): SVM-Maj: A
Majorization Approach to Linear Support Vector Machines with Different Hinge
Errors. Advances in Data Analysis and Classification.
− 54 −

Usage of Artificial Neural Networks for Data
Handling
Lars Frank Groe and Franz Joos
Helmut-Schmidt-University
University of the Federal Armed Forces Hamburg
Power Engineering
Laboratory of Turbomachinery
Hamburg, Germany
Abstract. To reduce the environmental pollution, it is essential to increase the
efficiency of the commercially available combustion engines. If one succeeds in designing
the combustion process, in particular the chemical reactions, it is feasible to
replace the experiment by computer simulations. Complex chemical reaction mechanisms
like the GRI3.0* consist of 325 reactions with 53 species. The computational
hardware costs limit the evaluation of integrals of stiff equations to simple problems
(2-D, low Reynolds numbers) or to very small numbers of species. Otherwise
turbulent combustion, for example in combustion chambers of gas turbines, often
consists of complex geometry with a wide spectrum of chemical states and proceeds
with high Reynolds numbers. The use of databases for storing chemical reactions
is widely described in literature [Pope]. Therefore several storage-based techniques
have been implemented for data mining (Look-up-table, In situ adaptive tabulation).
The use of artificial neuronal networks (ANN) to simulate complex chemistry
with full GRI3.0 is suggested in this paper. ANN can represent the chemical reactions
by creating a non-linear-multivariate model of the dataset. The information
of the dataset is stored in the weights of the connected neurons in the ANN. The
net is able to find the optimum approximation of the presented data by supervised
learning method called back propagation. The modeling and generalisation of large
number of chemical states by means of ANN with regard to complicated combustion
simulation is the purpose of this work.
References
[Pope] Pope, S.B.: Computationally efficient implementation of combustion chemistry
using in situ adaptive tabulation. In: Combust. Theory Modelling, Vol. 1
(1997), pp. 41-63
Gregory P. Smith, David M. Golden, Michael Frenklach, Nigel W. Moriarty, Boris
Eiteneer, Mikhail Goldenberg, C. Thomas Bowman, Ronald K. Hanson, Soonho
Song, William C. Gardiner, Jr., Vitali V. Lissianski, and Zhiwei: http://www.
me.berkeley.edu/gri_mech/.
− 55 −

Model diagnostics of finite mixtures using
bootstrapping
Bettina Grün 1 and Friedrich Leisch 2
1 Department für Statistik und Mathematik, Wirtschaftsuniversität Wien
Augasse 2-6, 1090 Wien, Austria; Bettina.Gruen@wu-wien.ac.at
2 Institut für Statistik, Ludwig-Maximilians-Universität München, Ludwigstraße
33, D-80539 München, Germany; Friedrich.Leisch@stat.uni-muenchen.de
Abstract. The EM algorithm provides a common framework for maximum likelihood
estimation of finite mixture models. The fitted models can differ with respect
to the component specific models and may also allow for concomitant variables to
model the component weights. The use of resampling methods to analyze finite
mixture models fitted with the EM algorithm is appealing, because the bootstrap
similarly to the EM algorithm constitutes a common framework for these models.
We will outline various possibilities to use resampling methods for model diagnostics
such as for determining the number of components, checking model identifiability
and analyzing the stability of induced clusterings.
The R package flexmix implements the EM algorithm for ML estimation of finite
mixture models. It provides the E-step and all data handling and arbitrary mixture
models can be fitted by modifying the M-step. The implementation of bootstrap
techniques to allow for model diagnostics of the models fitted with the package is
presented.
Key words: Booststrap, Finite mixture, Model diagnostics, Resampling
References
Grün, B. and Leisch, F. (2004): Bootstrapping Finite Mixture Models. In: J. Antoch
(Ed.): Compstat 2004—Proceedings in Computational Statistics. Springer,
Heidelberg, 1115–1122.
Hothorn, T., Leisch, F., Zeileis, A. and Hornik, K. (2005): The Design and Analysis
of Benchmark Experiments. Journal of Computational and Graphical Statistics,
14(3), 1–25.
Leisch, F. (2004): FlexMix: A general framework for finite mixture models and latent
class regression in R. Journal of Statistical Software, 11(8).
McLachlan, G.J. (1987): On Bootstrapping the Likelihood Ratio Test Statistic for
the Number of Components in a Normal Mixture. Applied Statistics, 36(3),
318–324.
− 56 −

Classification with Regularized Kernel
Mahalanobis-Distances
Bernard Haasdonk 1 and El˙zbieta P ↩ ekalska 2
1 Institute of Numerical and Applied Mathematics, University of Münster,
Germany haasdonk@math.uni-muenster.de
2 School of Computer Science, University of Manchester, United Kingdom
pekalska@cs.man.ac.uk
Abstract. Linear discriminant analysis has been demonstrated to be successful in
kernel-induced feature spaces. In particular, in terms of accuracy, the kernel Fisher
discriminant (KFDA) can frequently compete with or even outperform the support
vector machine (SVM) (Mika et al. 2000). In situations, where linear discrimination
in kernel feature space is suboptimal, nonlinear techniques offer a better solution
(Huang et al. 2005). An example is quadratic classification in the kernel space, based
on kernelized versions of class-related Mahalanobis distances.
In this presentation, we present two different formulations for quadratic classifiers
in kernel-induced feature spaces, depending whether the class-related covariance
operator has to be regularized or not. Experimental results on a toy data set enable
us to draw comparisons to SVM and KFDA. More importantly, these results provide
a proof of principle that nonlinear discriminants can be beneficial in the kernel
space.
Key words: Kernel Methods, Quadratic Discriminants, Kernel Mahalanobis-Distance
References
Mika, S., Rätsch, G., Schölkopf, B., Smola, A., Weston, J. and Müller, K.-R. (2000):
“Invariant feature extraction and classification in kernel spaces.” In S.A. Solla,
T.K. Leen, and K.-R. Müller (Eds.): Advances in Neural Information Processing
Systems 12. MIT Press, Cambridge, MA, 526–532.
Huang, S.-Y., Hwang, C.-R. and Lin, M.-H. (2005): “Kernel Fisher’s discriminant
analysis in Gaussian reproducing kernel Hilbert space,” Academia Sinica,
Taipei, Taiwan, Technical Report.
− 57 −

On classification of species of representation
rings
Lothar Häberle
Department of Biometry and Epidemiology, University of Erlangen-Nuremberg,
Waldstr. 6, 91054 Erlangen
Abstract. In biology and chemistry crystal structures and symmetries of molecules,
for example, are classified by mathematical groups. The assigned groups can then
be used to determine physical properties such as polarity and chirality.
Representations of groups as linear transformations of vector spaces and, more
generally, modules enables many group theoretical problems to be reduced to problems
of linear algebra, which is a well understood theory. Defining addition and
multiplication via direct sum and tensor product on the set of these modules and
then considering them as elements of a ring, the representation ring, is an approach
to examine such modules. In order to investigate representation rings one may study
their structure preserving maps to the complex numbers, which are called species.
We consider finite groups whose largest subgroup of prime power order is cyclic
for some prime number and study the corresponding representation ring. The indecomposable
modules are stated and the species are classified. The proposed way of
classification may be applied to other classes of groups in the future and then be used
in natural sciences. Throughout the paper we illustrate the theoretical statements
with examples.
Key words: mathematical group, species, representation ring, indecomposable
module
References
Benson, D.J. (1991): Representation and Cohomology I. Cambride Universtiy Press.
Fotsing, B. and Külshammer, B. (2005): Modular species and prime ideals for the
ring of monomial representations of a finite group. Communications in Algebra,
33, 3667–3677.
Green, J.A. (1962): The modular representation algebra of a finite group. Illinois
Journal of Mathematics, 6, 607–619.
Häberle, L. (submitted): The species and idempotens of the Green algebra of a finite
group with a cyclic Sylow subgroup.
Shriver, D.F. and Atkins, P.W. (2006): Inorganic Chemistry. Oxford University
Press.
− 58 −

Auswertung hochaufgelöster Streulichtdaten mit
Methoden der multivariaten Statistik
Cornelius Hahlweg und Hendrik Rothe
Helmut-Schmidt-Universität
Hamburg
Zusammenfassung. Die Entwicklung der Streulichtmeßtechnik wurde in den vergangenen
Dekaden, insbesondere mit Blick auf einen Einsatz in der Qualitätsprüfung
von Oberflächen, vorangetrieben. Streulichtverfahren erweisen sich für Oberflächenuntersuchungen
als besonders leistungsfähig, da sie berührungslos arbeiten, einen
skalierbaren Ausschnitt der Oberfläche prüfen und dabei feinste Oberflächenstrukturen
abbilden.
Prinzipiell liefern Streulichtverteilungen spektrale Aussagen über Eigenschaften
der untersuchten Oberfläche. Während diese für sehr glatte Oberflächen in der Tat
die Oberflächenfunktion selbst wiederspiegeln, erweisen sich für Oberflächen oberhalb
des sog. Rayleigh-Limits Methoden der multivariaten Statistik als sinnvoll.
Insbesondere können hier die höheren Momente der Streuverteilung als Merkmale
für Klassifikationsverfahren dienen. Während diese Momente in früheren Veröffentlichungen
als Beschreibungsform der Streuverteilung selbst unter eher heuristischen
Aspekten vorgeschlagen wurden, kann ihnen nunmehr auch eine physikalische Bedeutung
zugeordnet werden. Zur Vorverarbeitung und Reduktion der häufig sehr
umfangreichen zweidimensionalen Datenmengen kommt zunächst die Hauptkomponentenanalyse
(PCA) zum Einsatz. Zur Klassifikation verschiedener Proben, z.B. im
Sinne einer Qualitätskontrolle, wird die lineare kanonische Diskriminanzanalyse genutzt.
Der Beitrag gibt einen Einblick in die Grundlagen der verwendeten Verfahren
in Bezug zur Streulichtmeßtechnik und bietet Beispiele aus der Anwendung in der
Klassifikation technischer Oberflächen.
Literaturverzeichnis
Baier, D. and Gaul, W. (1999): Optimal Product Positioning Based on Paired Comparison
Data. Journal of Econometrics, 89, 365–392.
Bock, H.H. (1974): Automatische Klassifikation. Vandenhoeck & Ruprecht, Göttingen.
Brusch, M. and Baier, D. (2002): Conjoint Analysis and Stimulus Presentation:
A Comparison of Alternative Methods. In: K. Jajuga, A. Sokołowski and H.H.
Bock (Eds.): Classification, Clustering, and Analysis. Springer, Berlin, 203–210.
− 59 −

Algorithms for Computing the Multivariate
Isotonic Regression
Jürgen Hansohm 1
University of the Federal Armed Forces, Munich, Germany
Juergen.Hansohm@UniBw-Muenchen.de
Abstract. Sasabuchi (1983) introduces 1983 a multivariate version of the wellknown
univariate isotonic regression which plays a key role in the field of statistical
inference under order restrictions. His proposed algorithm for computing the multivariate
isotonic regression, however, is guaranteed to converge only under special
conditions (Sasabuchi 2003). In this paper, a more general framework for multivariate
isotonic regression is given and an algorithm based on Dykstra’s method is
used to compute the multivariate isotonic regression. Two numerical examples are
given to illustrate the algorithm and to compare the results with the Monte Carlo
simulation published by Fernando and Kulatunga (2007)
Key words: multivariate isotonic regression, projection, Dykstra’s algorithm, partial
order, least squares solution
References
S. Sasabuchi, M. Inutsuka, D.D.S. Kulatunga (1983): A multivariate version of isotonic
regression,Biometrika 70, 2 (1983) 465–472.
S. Sasabuchi, T. Miura, H. Oda (2003): Estimation and test of several multivariate
normal means under an order restriction when the dimension is larger than two,
Journal of Statistical Computation and Simulation 73, 9 (2003) 619–641.
W.T.P.S. Fernando, D.D.S. Kulatunga (2007): On the computation and some applications
of multivariate isotonic regression,Computational Statistics and Data
Analysis 52 (2007) 702–712.
− 60 −

Die präzise und effizienzte Erkennung von
medizinischen Anforderungsformularen
Uwe Henker 1 , Alfred Ultsch 2 , and Uwe Petersohn 3
1
DOCexpert Computer GmbH
Bamberg
u.henker@docexpert.de
2
Databionics Research Group
Philipps-University of Marburg, Germany
ultsch@informatik.uni-marburg.de
3
TU Dresden
Institut Künstliche Intelligenz
peterson@inf.tu-dresden.de
Abstract. Formulare für die Anforderung von medizinischen und/oder diagnostischen
Leistungen spielen eine grosse Rolle in der gegenwätigen medizinischen Praxis.
Mit solchen Formularen werden ggf. lebensentscheidende ärztliche oder labormedizinische
Leistungen für einen Patienten angefordert. Die Übertragung der vom Arzt
per Hand in ein solches Formular eingetragenen Anforderungen an die Labor- bzw.
Krankenhaus Informtionssysteme erfolgt dabei durch maschinelle Erkennungsverfahren
(Optical Marker Recognition (OMR)). Hierbei ist von einer sich ändernden
Menge von verschiedenen Formularen (Prototypen) auszugehen, die zuverlässig
erkannt werden müssen.
Der Beitrag beschreibt die Wissensrepräsentation derartiger Formulare in einer Falldatenbank
von Prototypen mittels Case based Reasoning (CBR). Zentrale Idee ist
dabei, die vorverarbeiteten und abstrahierten Bilder von gescannten Formularen mit
den Prototypen so zu vergleichen, dass Fehlertoleranzen zugelassen werden. Wird ein
neuer Prototyp in die Wissensbasis eingefügt, der eine grosse Überschneidung mit
bestehenden Prototypen hat, so wird in einem mehrstufigen Verfahren zusätzliches
Entscheidungswissen für den Formularklassifikator in der Wissensbasis repräsentiert.
Der Ansatz führt zu einer Erkennungsrate von 97% und keinen falsch-positiven
Fällen. Im Vergleich zu anderen veröffentlichten Ansätzen kann eine substantielle
Steigerung bei der Erkennungsleistung festgestellt werden. Insbesondere ist die Sonderanforderung,
dass keine falsch-positiven Ergebnisse erzeugt werden zu 100%
erfüllt. Das System wurde mit realen Formularen getestet, die in der Anwendung ein
eindeutiges Merkmal (Barcode) zur Identifizierung verwenden. Die Notwendigkeit
dieses Barcodes auf jedem Formular, stellt eine nicht unerhebliche Einschränkung
dar. Durch den hier beschriebenen Ansatz wird diese aufgehoben.
Key words: Classification, Knowledge Representation, Optical Marker Recognition,
Image Processing, Medical Information Systems
− 61 −

Using cluster analysis for species delimitation
Christian Hennig 1 and Bernhard Hausdorf 2
1 Department of Statistical Science, University College London, Gower St, London
WC1E 6BT, United Kingdom chrish@stats.ucl.ac.uk
2 Zoologisches Museum der Universität Hamburg, Martin-Luther-King-Platz 3,
20146 Hamburg, Germany hausdorf@zoologie.uni-hamburg.de
Abstract. Species delimitation is a fundamental task in biology. Operationally,
species can be conceived as continuously varying groups of organisms that are separate
from other such groups. This suggests methods of cluster analysis to delimit
species empirically for given data. However, in the literature there is no agreement
about the species concept (see Mayden, 1997, for an overview), which affects the
choice of the appropriate data, cluster analysis method, and the interpretation of
the results. A particular problem arises because of the hierarchical nature of evolution.
Clusters occur at many levels and may represent, beside species, intrapopulation
polymorphisms, populations, regional variation or higher taxa. We present
a methodology for delimiting putative species based on codominant and dominant
genetic markers. The method combines the definition of an appropriate dissimilarity
measure, multidimensional scaling and model-based cluster analysis. We propose a
null model taking into account spatial autocorrelation in order to check whether
inhomogeneities in the data can be explained from regional variation alone. The
methodology is a generalization of the techniques presented in Hennig and Hausdorf
(2004) to categorial genetic data. The methodology is compatible with most species
concepts. We discuss some general issues such as the choice of the clustering method
and joining of not well separated clusters, which rather indicate inhomogeneity on
lower levels than species.
Key words: Model-based cluster analysis, genotypes, spatial autocorrelation
References
Hennig, C. and Hausdorf, B. (2004): Distance-based parametric bootstrap tests for
clustering of species ranges. Computational Statistics and Data Analysis, 45,
875–896.
Mayden, R.L. (1997): A hierarchy of species concepts: the denouement in the saga
of the species problem. In: M.F. Claridge, H.A. Dawah, M.R. Wilson (Eds.):
The Units of Biodiversity. Chapman and Hall, London, 381–424.
− 62 −

Nonlinear Effects in PLS Path Models:
A Comparison of Available Approaches
Jörg Henseler 1
Institute of Management Research, Radboud University Nijmegen, Thomas van
Aquinostraat 1, 6525 GD Nijmegen, The Netherlands, J.Henseler@fm.ru.nl
Summary. Along with the development of scientific disciplines, researchers in business
and social sciences are increasing interested in investigating nonlinear effects
between latent variables. In this contribution, I present four approaches to modeling
nonlinear effects with PLS: Firstly, Wold’s (1982) original approach takes the nonlinearity
in the structural model into account during the iterative PLS algorithm.
Secondly, the product indicator approach developed by Chin, Marcolin, and Newsted
(2003) requires that the nonlinear function be applied a priori on the indicator
level. Thirdly, a two-stage approach as suggested by Henseler and Fassott (2008) estimates
the nonlinear effect a posteriori once the latent variable scores are estimated
by means of the linear effects PLS path model. Fourthly, I adapt an orthogonalizing
approach originally suggested by Little, Bovaird, and Widaman (2006) to nonlinear
PLS path modeling. Finally, I compare the performance of these four approaches
by means of a Monte Carlo simulation, and derive guidelines for users of PLS path
modeling.
Key words: partial least squares, PLS path modeling, nonlinear terms
References
Chin, W. W., Marcolin, B. L., and Newsted, P. N. (2003): A Partial Least Squares
Latent Variable Modeling Approach for Measuring Interaction Effects: Results
from a Monte Carlo Simulation Study and an Electronic-mail Emotion/Adoption
Study. Information Systems Research, 14, 189–217.
Henseler, J. and Fassott, G. (2008): Testing Moderating Effects in PLS Path
Models: An Illustration of Available Procedures. In: V. E. Vinzi, W. W. Chin, J.
Henseler, and H. Wang (Eds.): Handbook Partial Least Squares Path Modeling.
Springer, Heidelberg, forthcoming.
Little, T. D., Bovaird, J. A., and Widaman, K. F. (2006): On the Merits of
Orthogonalizing Powered and Product Terms: Implications for Modeling Interactions
Among Latent Variables, Structural Equation Modeling, 13, 497–519.
Wold, H. (1982): Soft Modeling. The Basic Design and Some Extensions. In: K.
G. Jöreskog and H. Wold (Eds.): Systems under Indirect Observation. Causality,
Structure, Prediction, Part I. North-Holland, Amsterdam, 1–54.
− 63 −

Classification of text processing components:
The Tesla Role System
Jürgen Hermes and Stephan Schwiebert
Linguistic Data Processing, Department of Linguistics, University of Cologne
{jhermes, sschwieb}@spinfo.uni-koeln.de
Abstract. The analysis of sequences of discrete tokens (i.e., texts) is a major research
subject of several essentially different sciences such as corpus linguistics,
literature and bioinformatics. Though differing in both data and its interpretation,
these sciences share some intermediate steps. Following these considerations, the obvious
procedure is to encapsulate text processing tasks into components and create a
framework that enables component interaction. The component arrangement within
a workflow is comparable to an experimental setup: it allows a gradual modification
of experiments, e.g., rerunning an experiment with a modified configuration or a
replaced component.
The Text Engineering Software Laboratory (Tesla) is an implementation of a
framework that supports the development and deployment of text processing components
as well as the execution of experiments on textual data. One of its main
ideas is reducing the framework’s restrictions on data modeling to a minimum, allowing
developers to focus on their scientific tasks. However, this results in new issues:
an extensible way of database access definition, data exchange between components
and data conversion during visualization. If, for instance, the annotations produced
by a component cannot be related sequentially to single text elements but do instead
represent more complex relations between these elements, as generally in graphs or
matrices, the information contained in such data types can only be extracted with
knowledge about their internal structure and its meaning, thus violating a basic
principle of component frameworks.
Addressing these concerns, the concept of a role is introduced in Tesla. A role
adopted by a component specifies the type as well as the access methods of the
produced data. As the role system implicitly exhibits a hierarchical structure, this
finally leads to a dynamic classification of text processing components.
− 64 −

Strengths and Weaknesses of Ant Colony
Clustering
Lutz Herrmann and Alfred Ultsch
Databionics Research Group
University of Marburg, Germany
{lherrmann,ultsch}@informatik.uni-marburg.de
Abstract. Ant colony clustering (ACC) is a promising nature-inspired technique
where stochastic agents perform the task of clustering high-dimensional data on a
low-dimensional output space. Most ACC methods are derivatives of the approach
proposed by Lumer and Faieta. These methods usually perform poorly in terms
of topographic mapping and cluster formation. In particular when compared to
clustering on Emergent Self-Organizing Maps (ESOM).
In order to address this issue, an unifying representation for both ACC methods
and Emergent Self-Organizing Maps is derived in a brief yet formal manner. ACC
terms are related to corresponding mechanisms of the Self-Organizing Map. This
leads to insights on both algorithms. ACC are considered as first-degree relatives of
the ESOM. This explains benefits and shortcomings of ACC and ESOM. Furthermore,
the proposed unification allows to judge whether modifications improve an
algorithm’s clustering abilities or not. This is demonstrated using a set of cardinal
clustering problems.
Key words: Clustering, Emergent Self-Organizing Maps, Swarm Intelligence
References
Deneubourg, J.-L., Goss, S., Franks, N., Sendova-Franks, A., Detrain, C. and Chretien,
L. (1991): The dynamics of collective sorting: Robot-like ants and ant-like
robots. In: Proc. of the First International Conference on Simulation of Adaptive
Behaviour: From Animals to Animats 1. MIT Press, Cambridge, 356-365.
Handl, J., Knowles, J. and Dorigo, M. (2005): Ant-Based Clustering and Topographic
Mapping. Artificial Life 12(1), MIT Press, Cambridge.
Kohonen, T. (1995): Self-Organizing Maps. Springer, Berlin, Heidelberg, New York.
Lumer, E. and Faieta, B. (1994): Diversity and adaption in populations of clustering
ants. In: Proc. of the Third International Conference on Simulation of Adaptive
Behaviour: From Animals to Animats 3. MIT Press, Cambridge, 501–508.
Ultsch, A. and Herrmann, L. (2005): The architecture of emergent self-organizing
maps to reduce projection errors. In: Verleysen M. (Eds): Proc. of the European
Symposium on Artificial Neural Networks (ESANN 2005).
− 65 −

Reconstructing Central Places and Settlements
Groups
Irmela Herzog
The Rhineland Regional Council / The Rhineland Commission for Archaeological
Monuments and Sites
Bonn, Germany
i.herzog@LVR.de
Abstract. If (i) the location of settlements is known for a certain period in time
and (ii) the settlements are distributed in such a way that cluster centres with high
settlement densities are present, then a variant of the density clustering algorithm
using basin spanning trees can be applied to (i) reconstruct the location of the
cluster centres and (ii) group the settlements. The model of this approach is based
on the assumption that the exchange rate of products is high where the settlements
are close to each other and/or the settlement size is large. If people living in a
settlement with a low exchange rate wanted to buy or sell something they would
walk to one of the settlements with higher exchange rates in their neighbourhood.
This can be modelled by several variants of the density clustering algorithm using
basin spanning trees. Which of the neighbouring settlements is determined as the
preferred location for product exchange depends on the algorithm variant chosen.
This method is used to reconstruct trade networks, and all settlements connected
by direct or indirect trade links constitute a group. While extending the original
clustering algorithm to support different settlement sizes could be accomplished
easily, the adjustments needed to take into account the costs of walking between
two locations in prehistoric times are by no means trivial. Examples from the river
Main area with Bronze and Iron Age settlements will be presented.
References
Hader, S., Hamprecht, F. A. 2003. Efficient Density Clustering Using Basin Spanning
Trees. In: M. Schader, W. Gaul, M. Vichi (Hrsg.), Between Data Science and
Applied Data Analysis. Studies in Classification, Data Analysis, and Knowledge
Organization (Berlin, Heidelberg, New York):39-48.
− 66 −

On the prognostic value of gene expression
signatures for censored data
Thomas Hielscher, Manuela Zucknick, Wiebke Werft and Axel Benner
Division of Biostatistics, German Cancer Research Center, Heidelberg, Germany
t.hielscher@dkfz.de
Abstract. As part of the validation of a new gene expression signature it is good
statistical practice to quantify the amount of prognostic information represented by
the signature. Open questions are how to measure the gain in prognostic information
compared to established clinical parameters or biomarkers and the additional
predictive accuracy especially when dealing with censored data. To answer these
questions it is required to use consistent and interpretable measures.
Several measures of prediction accuracy and proportion of explained variation
have been suggested for right-censored event times. The underlying mechanisms of
these measures are as different as the use of Schoenfeld residuals, model likelihoods
or the variation of the individual survival curves. Consequently, these measures vary
in their assumptions and properties and it remains unclear under which conditions
and to which extent they are comparable. Moreover, explained variation for survival
data can be considered as a function of time and therefore strongly depends on the
available follow-up time and the time range of interest.
We present a comparison of several common measures such as the Brier Score
(Graf et al., 1999), the V measure (Schemper and Henderson, 2001) and the method
of O’Quigley and Xu (2001) to illustrate their application to simulated and real
clinical data. A presentation of existing and possible approaches to estimate the
variability of these measures will be provided. An overview of available software
implementations in R will be given.
Key words: Survival, Predictive Accuracy, Gene Expression
References
Graf, E., Sauerbrei, W. F., Schmoor, C., and Schumacher, M. (1999): Assessment
and comparison of prognostic classification schemes for survival data. Statistics
in Medicine,18, 2529–2545.
O’Quigley, J. and Xu, R. (2001): Explained variation in proportional hazards regression.
In: J. Crowley and D.P. Ankerst (Eds.): Handbook of Statistics in Clinical
Oncology, Second Edition. Chapman & Hall/CRC Press, 347–363.
Schemper, M. and Henderson, R. (2000): Predictive accuracy and explained variation
in Cox regression. Biometrics, 56, 249–255.
− 67 −

Likelihood ratio testing for hidden Markov
models
Hajo Holzmann and Jörn Dannemann
University of Karlsruhe
Germany
Abstract. When a mixture arises as the marginal distribution of a stationary process,
the dependency structure can be incorporated by assuming that the underlying
regime forms a finite state Markov chain. This leads to the class of hidden Markov
models (HMMs), which are also called Markov dependent mixtures. We shall discuss
maximum likelihood inference in HMMs. In particular, we investigate the problem
of testing for the number of states via the likelihood ratio test (LRT). We propose
a modified LRT for two against more states in an HMM, which is based on the
so-called likelihood function under independence assumption, and derive its asymptotic
distribution under the null hypothesis. Simulation results and applications to
financial and biological time series illustrate the practical use of the methods.
− 68 −

Rule-Based Learning of Reliable Classifiers
Jens Hühn and Eyke Hüllermeier
Department of Mathematics and Computer Science, University of Marburg
{huehnj,eyke}@mathematik.uni-marburg.de
Abstract. This paper introduces a fuzzy rule-based classification method called
FR3, which is short for Fuzzy Round Robin RIPPER. As the name suggests, FR3
builds upon the RIPPER algorithm, a state-of-the-art rule learner. More specifically,
in the context of polychotomous classification, it uses a fuzzy extension of RIPPER
as a base learner within a round robin scheme and, thus, can be seen as a fuzzy
variant of the R3 learner that has recently been introduced in the literature. A
key feature of FR3, in comparison with its non-fuzzy counterpart, is its ability to
represent different facets of uncertainty involved in a classification decision in a more
faithful way. FR3 thus provides the basis for implementing “reliable classifiers” that
may abstain from a decision when not being sure enough, or at least indicate that
a classification is not fully supported by the empirical evidence at hand. Besides,
our experimental results show that FR3 outperforms R3 in terms of classification
accuracy and, therefore, suggest that it produces predictions that are not only more
reliable but also more accurate.
Key words: Machine learning, classification, rule induction, uncertainty, fuzzy sets.
References
William W. Cohen (1995). Fast effective rule induction. In Armand Prieditis and
Stuart Russell, editors, Proceedings of the 12th International Conference on
Machine Learning, pages 115–123, Tahoe City, CA. Morgan Kaufmann.
Johannes Fürnkranz (2003). Round robin ensembles. Intell. Data Anal., 7(5):385–
403.
Eyke Hüllermeier and Klaus Brinker (2008). Learning valued preference structures
for solving classification problems, Fuzzy Sets and Systems (to appear).
− 69 −

Combining Predictions in Pairwise
Classification: An Adaptive Voting Strategy
and Its Relation to Weighted Voting
Eyke Hüllermeier and Stijn Vanderlooy
Department of Mathematics and Computer Science, University of Marburg
{eyke,vanderlooy}@mathematik.uni-marburg.de
Abstract. Learning by pairwise comparison is a well-known decomposition technique
which allows one to transform a polychotomous classification problem into
a number of binary problems. To aggregate the predictions from the ensemble of
binary models into a final classification, various aggregation strategies have been
proposed. The most commonly used strategy is weighted voting, in which the prediction
of each model is counted as a (weighted) “vote” for a class label, and the
class with the highest sum of votes is predicted as the label of the query instance.
Even though weighted voting turned out to perform very well in practice, it remains
ad-hoc to some extent and lacks a sound theoretical basis.
In this regard, the current paper makes the following contributions. First, we
propose a formal framework in which the aforementioned aggregation problem can be
studied in a convenient way. This framework is based on the setting of label ranking
which has recently received attention in the machine learning literature. Second,
within this framework, we develop a new aggregation strategy called adaptive voting.
This strategy allows one to take the strength of individual learners into consideration
and, under certain assumptions, is provably optimal in the sense that it yields a MAP
prediction of the class label. Thirdly, we show that weighted voting can be seen as
an approximation of adaptive voting and, hence, approximates a MAP prediction.
This theoretical justification of weighted voting is confirmed by strong empirical
evidence showing that it is (at least) competitive in practice.
Key words: Machine learning, pairwise classification, weighted voting, label ranking,
MAP prediction.
− 70 −

Using Cluster Networks to Represent
Non-Compatible Sets of Clusters
Daniel H. Huson and Regula Rupp
Center for Bioinformatics ZBIT, Tübingen University, Sand 14, 72076 Tübingen,
Germany
huson,rupp@informatik.uni-tuebingen.de
Abstract. A set of clusters is called compatible (or hierarchical), if it can be represented
by a rooted tree. In many applications, such as multiple gene phylogenetic
analysis, sets of clusters arise that are not compatible and the question arises how
to represent such sets in a useful way, in particular emphasizing parts of the cluster
system that are tree-like and where the incompatibilities lie.
The result of a multiple gene tree analysis is usually a number of different tree
topologies that are each supported by a significant proportion of the genes. We
introduce the concept of a cluster network that can be used to combine such trees
into a single rooted network, which can be drawn either as a cladogram or phylogram.
In contrast to split networks, which can grow exponentially in the size of the input,
cluster networks grow only quadratically. A cluster network is easily computed using
a modification of the tree-popping algorithm, which we call network-popping. The
approach will be made available as part of the Dendroscope tree-drawing program
and its application will be illustrated using data and results from recent studies on
large numbers of gene trees.
Key words: clusters, networks, trees, phylogenetics
References
D.H. Huson and D. Bryant. Application of phylogenetic networks in evolutionary
studies. Molecular Biology and Evolution, 23:254–267, 2006. Software available
from www.splitstree.org.
D.H. Huson, D.C. Richter, C. Rausch, T. Dezulian, M. Franz, and R. Rupp. Dendroscope:
An interactive viewer for large phylogenetic trees. BMC Bioinformatics,
8:460doi:10.1186/1471-2105-8-460, 2007. Software available from
www.dendroscope.org.
− 71 −

Genome phylogeny based on short-range
correlations in DNA sequences
Marc-Thorsten Hütt 1
Jacobs University Bremen
School of Engineering and Science
Campus Ring 1
m.huett@jacobs-university.de
Abstract. The surprising fact that global statistical properties computed on a
genomewide scale may reveal species information has first been observed in studies
of dinucleotide frequencies. In this presentation I will look at the same phenomenon
with a totally different statistical approach. We show that patterns in the shortrange
statistical correlations in DNA sequences serve as evolutionary fingerprints of
eukaryotes. All chromosomes of a species display the same characteristic pattern,
markedly different from those of other species. The chromosomes of a species are
sorted onto the same branch of a phylogenetic tree due to this correlation pattern.
The average correlation between nucleotides at a distance k is quantified in two independent
ways: (i) by estimating it from a higher-order Markov process and (ii) by
computing the mutual information function at a distance k. We show how the quality
of phylogenetic reconstruction depends on the range of correlation strengths and
on the length of the underlying sequence segment. This concept of the correlation
pattern as a phylogenetic signature of eukaryote species combines two rather distant
domains of research, namely phylogenetic analysis based on molecular observation
and the study of the correlation structure of DNA sequences.
− 72 −

Dimensionality Reduction of Similarity Matrix
Tadashi Imaizumi
Tama University imaizumi@tama.ac.jp
Abstract. We have become easily collecting a similarity data matrix of large number
of objects, for example, in Basket Analysis in Data Mining. Then we apply
several unsupervised methods to this data according to our purpose of analysis It is
shown in many research fields that the geometric models such as MultiDimensional
Scaling(MDS) or Self-Organizing Map(SOM) are applicable. However, we have two
problems when we want to apply these methods to a large similarity matrix. One
will be the change of dimensions focused, The other one is on how to employ our
prior information about data. The latent dimensions of the similarity evaluation
process may be common to all objects when the attributes of objects is less ambiguous
and number of objects is not so large. However, we will not agree with that
similarity evaluation between Hamburg and Tokyo is same to that between Hamburg
and Berlin. This requires us how to model this process. The other one is how
to employ the research’s knowledge as the prior information to the model. We have
some knowledge about data and the gathered data will be containing these as the
hidden information of data. It will contribute to propose some supervised geometric
model treating those information as model parameters. I will discuss these two
problems, compare the dimensionality reduction methods, and propose the model
of the change of dimensions focused and the prior information about data.
Key words: supervised,the latent dimensions, the attribute focus
References
Koh¨nen, T.(1995): Self-Organizing Maps. Springer, Berlin, Heidelberg.
Roweis, S. T. and Saul, L. K. (2000): Nonlinear dimensionality reduction by locally
linear embedding. Science 290, 2323-2326.
Sammon, J. W., Jr. (1969): A nonlinear mapping for data structure analysis. IEEE
Transactions on Computers, C-18, 5-28.
Tenenbaum, J. B., de Silva, V. and Langford, C. (2000). A global geometric framework
for nonlinear dimensionality reduction. Science 290, 2319-2323.
− 73 −

Siedlungsverhalten währende des 7. -11.
Jahrhunderts entlang der Ems. Eine GIS
gestützte siedlungsarchäologische Analyse des
Raumes zwischen Warendorf und Rheine
Katrin Jaspers
Universität Münster, Germany
Zusammenfassung. Mit Hilfe von GIS sollen chronologische, topographische und
historische Zusammenhänge zwischen den verschiedenen Siedlungen des Untersuchungsraums
dokumentiert werden. Dabei werden auch pedologische Gesichtspunkte
einbezogen. So könnten Abläufe und Entwicklungen der Besiedlung deutlich gemacht
und möglicherweise Rückschlüsse auf die Infrastruktur zwischen den Siedlungen gezogen
werden.
Da sich die Arbeit noch in der Entwicklungsphase befindet, kann hier nur ein
vorläufiger thematischer Abriss gegeben werden.
− 74 −

Benchmarking Bicluster Algorithms
Sebastian Kaiser and Friedrich Leisch
Department of Statistics, Ludwig-Maximilians-Universität München,
Ludwigstrasse 33, 80539 München, Germany,
firstname.lastname@stat.uni-muenchen.de
Abstract. Over the last decade, bicluster methods have become increasingly popular
in different fields of two way data analysis, and a large variety of algorithms
and analysis methods have been published, see (Madeira and Oliveira, 2004) for
a survey. In this presentation, we show how the general benchmarking framework
by Hothorn et al (2005) can be adapted to the special case of biclustering. A key
issue is the development of bootstrap strategies for two-way data, which do not only
resample cases, but also variables.
All methods presented have been implemented in the open source R package
biclust, which is available on http:\\cran.r-project.org. Both artificial as well
as real world microarray data are used for benchmark experiments. The resulting
benchmark data are explored using new graphical techniques and analyzed by means
of statistical inference.
Key words: Biclustering, Two-Way-Clustering, Validation, R
References
Hothorn, T, Leisch, F., Zeileis, A., and Hornik K. (2005): The design and analysis
of benchmark experiments. Journal of Computational and Graphical Statistics,
14(3), 675–699.
Madeira, S. C. and A. L. Oliveira (2004): Biclustering algorithms for biological data
analysis: A survey. IEEE/ACM Transactions on Computational Biology and
Bioinformatics, 1(1),24–45.
Santamaria, R., Theron, R., and Quintales, L. (2007): A framework to analyze biclustering
results on microarray experiments. In: 8th International Conference on
Intelligent Data Engineering and Automated Learning (IDEAL’07) ,Springer,
Berlin, 770–779.
Turner, H., Bailey, T., and Krzanowski, W. (2005): Improved biclustering of microarray
data demonstrated through systematic performance tests. Computational
Statistics and Data Analysis, 48,235–254.
− 75 −

Nonparametric distribution analysis for text
mining
Alexandros Karatzoglou 1 , Ingo Feinerer 2,3 , and Kurt Hornik 3
1 INSA de Rouen, France alexis@ci.tuwien.ac.at
2 Theory and Logic Group, Institute of Computer Languages
Vienna University of Technology, Austria feinerer@logic.at
3 Department für Statistik und Mathematik,
Wirtschaftsuniversität Wien, Austria kurt.hornik@wu-wien.ac.at
Abstract. A number of new algorithms for non-parametric distribution analysis
based on Maximum Mean discrepancy measures and the Hilbert-Smith Norm have
been recently introduced. These novel algorithms operate in Hilbert space and can be
used for Two-Sample Tests, Hierarchical Clustering and Dimensionality Reduction.
Coupled with recent advances in string kernels, these methods extend the scope of
kernel-based methods in the area of text mining.
We review this group of kernel methods focusing on text mining where we will
propose novel applications and present an efficient implementation in the kernlab
package. We also present an efficient and integrated environment for applying modern
machine learning methods to complex text mining problems through the combined
use of the tm (for text mining) and the kernlab (for kernel-based learning) R
packages.
Key words: kernel methods, text mining, R
References
Karatzoglou A., Smola A. , Hornik K., Zeileis A. (2004): kernlab - An S4 Package
for Kernel Methods in R. Journal of Statistical Software, 11, 9
Smola, A., A. Gretton, L. Song and B. Schölkopf (2007): A Hilbert Space Embedding
for Distributions. Proceedings of the 18th International Conference on
Algorithmic Learning Theory (ALT 2007), 13-31, Springer, Berlin, Germany
Song, L., A. J. Smola, K. Borgwardt and A. Gretton (2007): Colored Maximum Variance
Unfolding. Proceedings of the Twenty-First Annual Conference on Neural
Information Processing Systems (NIPS 2007), 1-8, MIT Press, Cambridge,
Mass., USA
− 76 −

Indexnachbildende Wertpapiere –
Eine vergleichende Betrachtung am Beispiel
des DAX
Christian Klein 1 and Dennis Kundisch 2
1
Universität Hohenheim, Lehrstuhl für Rechnungswesen und Finanzierung, 70593
Stuttgart cklein@uni-hohenheim.de
2
Universität Augsburg, Lehrstuhl für BWL, Wirtschaftsinformatik und Financial
Engineering, 86135 Augsburg dennis.kundisch@wiwi.uni-augsburg.de
Abstract. In dieser Arbeit vergleichen wir verschiedene indexnachbildende Wertpapiere.
Produkte dieser Art versprechen dem Anleger eine Wertentwicklung, die einem
zugrunde liegenden Index möglichst exakt entspricht. Bei unserer Untersuchung betrachten
wir verschiedene Aspekte, unter anderem die Replikationsgüte der Papiere.
Somit liefern wir ein differenziertes Bild, sowohl über die Qualität der Produkte
als auch über die Stärken und Schwächen der üblicherweise angewendeten Untersuchungsverfahren.
Key words: Aktienindex, Indexreplikation
− 77 −

Polyphasic genomic approach for the taxonomy
of archaea and bacteria
Hans-Peter Klenk
DSMZ - German Collection of Microorganisms and Cell Lines 38124
Braunschweig,Germany hpk@dsmz.de
Abstract. Contemporary taxonomic classification of prokaryotes is primarily based
on the analysis of 16S rDNA sequences, extended by chemotaxonomical analyses,
e.g. whole cell fatty acids or amino acid analysis of cell walls. Although rDNAs
are excellent taxonomic markers, they represent far less than 1With meanwhile 637
published prokaryotic genomes and more than 1850 ongoing archaeal and bacterial
genome sequencing projects, the future of systematics will clearly be based on the
analysis of whole genome sequences. The major imminent problems on the way to
a genome-based systematic classification of prokaryotes are: 1) uneven phylogenetic
distribution of the sequenced genomes; 2) large variation of the phylogenetic value
in different fractions of the genomes; and 3) affordable technology for rapid sequence
generation combined with highly automated analysis of the information. A massive
generation of genome sequences from phylogenetically isolated archaea and bacteria
in a collaboration between Joint Genome Institute with DSMZ aims for rapid filling
of the deep phylogenetic gaps, soon to be followed by sequenced genomes of all
type strains. The fast variation between genes or sets of genes in view of sequence
conservation and genetic stability is problematic for global approaches to universal
phylogenies, but provides suitable novel taxonomic markers for more restricted areas
within the diversity of micro-organisms. New technologies for sequence generation
have already sharply decreased the price for the production of microbial genome
sequences and will continue to do so till the genome of any cultivated species of
archaea or bacteria will become affordable. The more complex problem to be solved
is the automated processing of the genomes within an endlessly growing sequence
space.
References
Klenk, H.-P. (2007) Genomic future for the taxonomy of prokaryotes. In: E Stackebrandt,
M Wozniczka, V Weihs & J Sikorski (eds) Connections between Collections.
Proceedings of the 11th International Conference on Culture Collections.
ISBN 978-3-00-022417-1. DSMZ, Braunschweig, Germany. pp 117-119
− 78 −

Exploiting synergetic and redundant features
for multimedia document classification
Jana Kludas, Eric Bruno and Stephane Marchand-Maillet
University of Geneva, Switzerland
kludas|bruno|marchand@cui.unige.ch
Summary. Multimedia data handling in all kinds of applications, received in the
last decade a lot of attention by the research communities due to the ’multimediatisation’
of e.g. the WWW and other data collections in all day life. The most
important problems identified in multimedia-based classification are amongst others
the high dimensionality of the multi modal feature space, the unknown and varying
relevance of features and modalities towards the class label, noise and missing values
in the input data and the semantic gap between low-level features and high level
semantic meanings.
We are working on a promising way to tackle many of these problems at once:
the calculation and exploitation of feature information interactions for feature selection
and construction in high dimensional feature spaces towards more efficient
information fusion and hence improved multimedia document classification. This
information-theoretic dependence measure finds the exact, irreducible attribute interactions
in a multivariate feature subset. Its definition is a stable relation because
information interactions are described by the information exclusively shared by this
subset’s variables. For subsets of size N = 2 the interaction is resulting in the well
known mutual information.
Then for higher order subsets N > 2, feature information interaction develops
its most important characteristic, it can result in positive and negative values. This
allows to discriminate two different types of feature relationships: (1) synergy given
by positive interactions and (2) redundancy indicated by negative ones. That be
used to treat the features of each of the types of interactions separately with the
help of specialized feature selection and construction strategies.
With the help of artificial data sets we will show what relationships information
interactions can detect. Classification experiments on real world data will also
show the superiority of preprocessing based on N-way interactions over pair-wise
dependence measures that are often used in recent feature selection approaches like
correlation and mutual information.
Key words: feature selection, multi modal information fusion, multimedia object
classification
− 79 −

Time-Varying Parameters in Brand Choice
Models
Thomas Kneib 1 , Bernhard Baumgartner 2 , and Winfried J. Steiner 3
1 Department of Statistics, University of Munich, Germany
thomas.kneib@stat.uni-muenchen.de
2 Department of Marketing, University of Regensburg, Germany
bernhard.baumgartner@wiwi.uni-regensburg.de
3 Department of Marketing, Technical University of Clausthal, Germany
winfried.steiner@tu-clausthal.de
Abstract. Brand Choice Models are frequently used in marketing research. In most
applications, estimated parameters representing customers’ reactions to, e.g., price
and promotional activities or brand-specific effects are assumed to be constant over
time. Marketing theories as well as experiences of marketing practitioners, however,
suggest the existence of trends and/or short-term fluctuations in brand choice behavior.
For example, price elasticities or preferences for certain brands may change in the
run-up to special events like Christmas or Mother’s day (e.g., Baumgartner 2003).
In this contribution, we employ multinomial logit models with varying coefficients to
estimate time-varying parameters in brand choice models. Both time-varying preferences
(trends) and time-varying effects of covariates are modeled based on penalised
splines, a flexible yet parsimonious nonparametric smoothing technique (e.g., Eilers
and Marx 1996). The estimation procedure is fully data-driven, determining the flexible
function estimates as well as the corresponding degree of smoothness in a unified
approach (e.g., Kneib 2006). Preliminary results suggest that the model considering
time-variable parameters outperforms models assuming constant parameters in
terms of fit and predictive validity.
Key words: Brand Choice, Multinomial logit model, Time-varying effects, Semiparametric
regression, P-splines
References
BAUMGARTNER, B. (2003): Measuring Changes in Brand Choice Behavior.
Schmalenbach Business Review, 55, 242–256.
EILERS, P.H.C. and MARX, B.D. (1996): Flexible Smoothing Using B-Splines and
Penalized Likelihood (with Comments and Rejoinder) Statistical Science, 11(2),
89–121.
KNEIB, T. (2006): Mixed Model Based Inference in Structured Additive Regression.
Dr. Hut-Verlag, München.
− 80 −

Multivariate comparative analysis of stock
exchanges - the European perspective
Julia Koralun-Bere´znicka
Maritime University in Gdynia, Morska 81-87, 81-225 Gdynia, Poland
koral@am.gdynia.pl
Abstract. The aim of the research is to perform a multivariate comparative analysis
of 20 European stock exchanges in order to identify the main similarities between
the objects. The basis of comparison is a set of 48 monthly variables from the period
01.2003–12.2005. The variables are classified into three categories: size of the market,
equity trading and bonds. The paper aims at identifying the clusters of alike
stock exchanges and at finding the characteristic features of each of the distinguished
groups. The obtained categorization to some extent corresponds with the division
of the European Union into ‘new’ and ‘old’ member countries. Clustering method,
performed for each quarter separately, also reveals that the classification is fairly
stable in time. The factor analysis, which was carried out to reduce the number of
variables, reveals three major factors behind the data, which are related with the
earlier mentioned categories of variables.
Key words: stock exchanges, cluster analysis, factor analysis
References
Boillat, P., de Skowronsky, N., Tuchschmid, N. (2002) Cluster analysis: application
to sector indices and empirical validation, Swiss Society for Financial Market
Research, 16, 467–486.
Kearney C., Lucey B. M., (2004) International equity market integration: Theory,
evidence and implications, “International Review of Financial Analysis”, 13,
571–583.
Kim S. J., Moshirian F., Wu E. (2005) Dynamic stock market integration driven by
the European Monetary Union: An empirical analysis, “Journal of Banking &
Finance”, 29, 2475–2502.
Krzanowski, W. J. (1988) Principles of multivariate analysis, Oxford University
Press, Oxford.
Morrison, D., (1967), Multivariate statistical methods, New York: McGraw-Hill.
Pascual A. G., (2003) Assessing European stock markets (co)integration, “Economics
Letters”, 78, 197–203.
− 81 −

Strategies of model construction for
the analysis of judgment data
Sabine Krolak-Schwerdt
Faculty of Humanities, Arts and Educational Science, University of Luxembourg
sabine.krolak@uni.lu
Abstract. This paper is concerned with the types of models researchers use to
analyze empirical data in the domain of social judgments and decisions. Examples of
this research domain are organizational or medical expert judgments, court decisions
or judgments in private everyday life.
Models for the analysis of judgment data may be divided into two classes depending
on the criteria they optimize. The first class consists of approaches which
optimize an internal (mathematical) criterion function. The aim is to minimize the
discrepancy of values predicted by the model from obtained data by use of, e.g., a
least squares approach. The second class comprises approaches which incorporate
a substantive underlying theory into the model. These accounts were developed to
satisfy external validity criteria, especially construct validity. Model parameters are
not only formally defined, but they represent specified components of judgments.
Several models from both classes are applied to a number of empirical data sets
and comparatively evaluated as to goodness-of-fit, variance accounted for by the
models and construct validity. Results exhibit considerable differences between the
two model classes in construct validity, but not in internal validity criteria.
It may be concluded that any model for the analysis of judgment data implies
the selection of a formal theory about judgments. Hence, optimizing a mathematical
criterion function does not induce a non-theoretical rationale or neutral tool.
Rather, this approach yields another formal theory about judgments which may not
correspond to substantive theories and, in this respect, may yield artefacts. As a
consequence, models satisfying construct validity seem superior in the domain of
judgments and decisions.
Key words: Models of data analysis, external validity, internal validity, model
comparison
− 82 −

An application of copula functions to market
risk management
Katarzyna Kuziak
Department of Financial Investments and Risk Management
Wroclaw University of Economics
ul. Komandorska 118/120, 53-345 Wroclaw, Poland
katarzyna.kuziak@ae.wroc.pl
Abstract. Modeling dependence is one of the main issues in risk management. From
risk management point of view, failure to model correctly tail-dependence may cause
many problems (under- or overestimation of risk level). The most popular approach
to model dependence between individual risks is based on classical correlation, but
in recent years an increasing interest in applying copula functions has arose. Copula
functions, a powerful concept to aggregate risks, has been introduced in finance by
Embrechts, McNeil, and Straumann. The aim of this paper is to provide simple
applications for the practical use of copulas for risk management from market risk
point of view. First, we introduce copula concept. Then, some applications of copulas
for market risk are given. Two Value at Risk estimation approaches are compared for
a portfolio of risks: utilizing classical covariance and copula-based one. The criterion
for evaluating performance of the two approaches is just the result of a VaR backtesting
procedure
Key words: financial dependence, copula functions, risk management, market risk,
Value at Risk
References
Cherubini U., Luciano E., Vecchiato W. (2004): Copula Methods in Finance, John
Wiley & Sons, New York.
Embrechts P., Frey R., McNeil A. (2005): Quantitative Risk Management: Concepts,
Techniques, and Tools, Princeton University Press
Embrechts P., Lindskog F., McNeil A. (2001): Modelling dependence with copulas
and applications to risk management, report, ETHZ Zurich.
Embrechts P., McNeil A., Straumann D. (1999): Correlation and dependence in risk
management: properties and pitfalls. In: Risk Management: Value at Risk and
Beyond (M. Dempster, Ed.) Cambridge University Press, Cambridge, 176-223.
Nelsen R. (1999): An introduction to copulas, Springer Verlag, New York.
− 83 −

Testing preference rankings
Kar Yin Lam 1 , Alex J. Koning 2 , and Philip Hans Franses 2
1
ERIM & Econometric Institute, Erasmus University Rotterdam, The
Netherlands
kylam@few.eur.nl
2
Econometric Institute, Erasmus University Rotterdam, The Netherlands
koning@few.eur.nl
franses@few.eur.nl
Abstract. Preference rankings are a common tool in consumer surveys. Such rankings
are easy to perform and the outcomes are easy to understand. In this study
we propose a method to examine if observed rankings imply statistically significant
differences across the products. If there is statistical evidence of differences across
products, the question is which products it concerns. We use multiple comparison
procedures to test which products are significantly different from each other. Our
method concerns the often-encountered practical situation that consumers evaluate
N products but only give preference rankings for a subset that is selected by each
consumer. This is due to the fact that the literature shows that the task of comparing
all N products could be too difficult. It may also be that the assignment of ranks
itself is problematic. For instance, ties may occur, that is, the consumer is indifferent
between products, and hence two or more products have the same rank. There
may also be missing values, that is, the consumer excludes a certain product in the
consideration set, and thus does not evaluate it. As a consequence the consumer is
not able to assign a rank to this product. The method we propose and analyze in
this paper does not suffer from these drawbacks. We illustrate it for 93 individuals
who rank 10 movies released in 2007 and who indicate preferences for only 4 of these
10 movies.
Key words: Rankings, Multiple comparisons, Ties, Missing observations
− 84 −

Bayesian Methods for Graph Clustering
Pierre Latouche, Christophe Ambroise, and Etienne Birmelé
Laboratoire Statistique et Génome (UMR CNRS 8071, INRA 1152, UEVE), La
Genopole Tour Evry 2, 523 place des Terrasses, 91000 Evry, France
firstname.lastname@genopole.cnrs.fr
Abstract. Networks are used in many scientific fields such as biology, social science,
and information technology. They aim at modeling, with edges, the way objects of
interest, represented by vertices, are related to each others. Looking for clusters,
also called communities or modules, of highly connected vertices, has appeared to
be a powerful approach to capture the underlying structure of a network.
Recently, the Erdős-Rényi Mixture model for Graph (ERMG) for community
detection was proposed by Daudin et al. (2006) with an associated algorithm, based
on variational techniques, for maximum likelihood estimation. Given a network, the
number of clusters is estimated and for all the vertices, the algorithm infers the
probability of membership to each cluster.
Following Hofman and Wiggins (2007), we show how the ERMG model can be
described in a full Bayesian framework. Then, we apply two families of approximation
techniques, called Variational Bayes (VB) and Expectation Propagation (EP),
for the inference procedure. Using simulated and real data sets, we compare both
the number and the quality of the estimated clusters obtained with the different
approaches.
Key words: Graph clustering, Variational Bayes, Expectation Propagation
References
Daudin, J. and Picard, F. and Robin, S. (2006): A Mixture Model for Random
Graphs. Tech. rep, INRIA.
Hofman, J.M. and Wiggins, C.H. (2007): A Bayesian Approach to Network Modularity.
ArXiv e-prints.
Jordan, M. and Ghahramani, Z. and Jaakkola, T. (1998): An introduction to variational
methods for graphical models. In: Jordan, M.: Learning in Graphical
Models. MIT Press.
− 85 −

Fundamental Indexation - testing the concept in
the German stock market
Hermann Locarek-Junge 1 and Max Mihm 1
Lehrstuhl für Finanzwirtschaft und Finanzdienstleistungen,
TU Dresden, D-01062 Dresden, Germany, locarekj@finance.wiwi.tu-dresden.de
Abstract. In Germany Fundamental Indexation is a rather new concept of portfolio
management, creating portfolios not based on market capitalization, but by other
economic numbers as revenues, employees, dividends or book value. The concept
is rather new and has been implemented in only some mutual investment funds
world wide so far. However, backward calculation of portfolios using the concept of
fundamental indexation (CFI) on time series from 1961 to 2004 for stock portfolios
in the US capital market and other studies show potential significant returns and
impressive sharpe ratios for this period (see Arnott/Sautter/Siegel 2007).
Trying to explain above average returns using factor models has not yet been
accomplished in a way that is compatible with traditional capital market theory. The
pro’s and con’s of the approach are discussed controversially between scientists and
practitioners, e.g.: ”[CFI] are a triumph of marketing, not of new ideas” (Fama 2007),
”With the advent of fundamental indexes we’re at the brink of a huge paradigm shift.
... [They] are the next wave of investing.” (Siegel 2006), and ”Fundamental Indexing
is just a new label on old wine.” (Asness 2006)
We use data from 1987 to 2007 in the german stock market and several indexing
concepts to test the CFI for the German market. We create and compare portfolio
clusters of market weighted, equally weighted and fundamentally weighted stocks.
We use Fama’s 3-factor-model to analyze and explain returns and anomalies, and
we question the persistence of investment returns using the CFI.
Key words: fundamental indexation, market index, portable alpha
References
Arnott, R., Sautter, G., Siegel, J. (2007): Fundamental Indexing Smackdown, in:
Journal of Indexes, Vol. 10, No. 5, pp. 10–15.
− 86 −

Identifying Atypical Cases in Kernel Fisher
Discriminant Analysis by using the Smallest
Enclosing Hypershere
Nelmarie Louw, Morne Lamont and Sarel Steel
Department of Statistics and Actuarial Science, University of Stellenbosch, Private
Bag X1, 7602 Matieland, South Africa. nlouw@sun.ac.za
Abstract. Kernel methods are fast becoming standard tools for solving classification
and regression problems in statistics. An example of a kernel based classification
method is Kernel Fisher discriminant analysis (KFDA). Conceptually KFDA entails
transforming the data in the input space to a high-dimensional feature space, followed
by linear discriminant analysis (LDA) performed in feature space. Although
the resulting classifier is linear in feature space, it corresponds to a non-linear classifier
in input space. However, as in the case of LDA, the classification performance
of KFDA deteriorates in the presence of atypical data points. Louw et al. (2007)
proposed several criteria for identification of atypical cases in KFDA. In extensive
simulation studies these criteria have been found to be successful, in the sense that
the error rate of the KFD classifier based on the dataset after removal of atypical
cases, is lower than the error rate of the KFD classifier based on the entire data
set. A disadvantage is that these criteria are calculated on a leave-one-out basis,
which becomes computationally prohibitive when dealing with large data sets. In
this paper we propose a two-step procedure for identifying atypical cases in large
data sets. Firstly, a subset of potentially atypical data cases is found by constructing
the smallest enclosing hypersphere (for each group) in feature space. Secondly, the
proposed criteria are employed to identify atypical cases, but only cases in the subset
are considered on a leave-one-out basis, leading to a substantial reduction in computation
time. We investigate the merit of this new proposal in a simulation study,
and compare the results to the results obtained when not using the hypersphere as
a first step. We conclude that the new proposal has merit.
Key words: Classification, Discriminant Analysis, Kernel Methods
References
Louw, N., Lamont, M.C. and Steel, S.J. (2007): Identification of Influential Cases
in Kernel Fisher Discriminant Analysis. In: P. Mantovan, A. Pastore and S.
Tonellato (Eds.): Complex Models and Computational Intensive Methods for
Estimation and Prediction. CLEUP EDITORE, 296–301.
− 87 −

Latent growth models for analyzing a multi
partner reward program
Karsten Lübke 1 and Heike Papenhoff 2
1 Customer Intelligence, Karstadt Warenhaus GmbH, Theodor-Althoff-Strasse 2,
45133 Essen karsten.luebke@karstadt.de
2 Ruhr-Universität Bochum, Lehrstuhl für Betriebswirtschaftslehre, insbesondere
Marketing, Universitätsstraße 150, 44780 Bochum
Abstract. In recent years, multi partner reward programs (MPRP) have enjoyed a
steady increase in popularity. However, one main advantage of MPRPs has not been
sufficiently researched: participating customers are expected to not only prefer their
focal card-issuing company over its competitors, but also to prefer other MPRP
partner companies over their resp. competitors outside the program. This so-called
cross-buying (CB) extended effect is crucial for suppliers when they evaluate program
participation. As this CB is a dynamic process which may change over time we
applied Latent Growth Models to analyze the effects of a MPRP on cross-buying.
Keywords
Latent Growth Models, Structural Equation Modeling, Cross Buying
− 88 −

Applying Statistical Models and Parametric
Distance Measures for Music Similarity Search
Hanna Lukashevich, Christian Dittmar, and Christoph Bastuck
Fraunhofer IDMT, Langewiesener Str. 22, 98693 Ilmenau, Germany
{lkh;dmr;bsk}@idmt.fraunhofer.de
Abstract. Content-based music similarity search implies methods that can be used
for finding music pieces close in perceptual semantic meaning. It is an inherent part
of automatic music recommendation systems and playlist generation. Most stateof-the-art
music similarity techniques use short-term acoustic features. Defining a
similarity measure between two audio signals consisting of multiple feature vector
frames still remains a challenging task. A multitude of related studies propose
the application of parametrical statictical models (e.g. Gaussian Mixture Models -
GMMs) in conjunction with suitable model distance measures. This approach has
several advantages: it enables a very compact and informative representation of an
audio signal and it allows similarity estimation solely based on the parameters of
the models. In this paper we concentrate only on those distance measures that do
not use computationally demanding sampling (like Monte Carlo or likelihood ratio
tests). A good example of such parametric distance measures is a Kullback-Leibler
divergence (KL-divergence), describing the distance between two single gaussians.
Unfortunately, the KL-divergence between GMMs is not analytically tractable. In a
recent ICASSP paper Hershley and Olsen presented several approximations of the
KL-divergence between two GMMs with very promising results. Hélen and Virtanen
proposed a Euclidean distance between GMMs ommiting the KL-divergence.
We present a KL-Euclidean Hybrid distance between GMMs. We compare it to
other state-of-the-art distance measures and show that it significantly outperforms
the others for several features and models. Rather then trying to find the best theoretical
approximation, our focus is on the best performance for music similarity
task. Besides that, we investigate the influence of the model parameter estimation
on the performance in musci similarity search. Here we compare the performance
for several versions of GMMs: a trivial model having just one gaussian per music
piece, GMMs with a fixed number of gaussians, and GMMs where the number of
components is estimated using model selection techniques. We also find promising
results using semantic information like song segmentation. In the latter case, we
model each segment of the song with a single gaussian and represent it as a GMM,
depending on the duration of the segments.
Key words: music information retrieval, music similarity, Gaussian mixture models,
Kullback-Leibler divergence
− 89 −

Determining the number of components in
mixture models for hierarchical data
Olga Lukociene 1 and Jeroen K. Vermunt 2
1
Tilburg University PO Box 90153 5000 LE Tilburg The Netherlands
o.lukociene@uvt.nl
2
Tilburg University PO Box 90153 5000 LE Tilburg The Netherlands
j.k.vermunt@uvt.nl
Abstract. Recently, various types of mixture models have been developed for data
sets having hierarchical or multilevel structure (see, e,g., Vermunt 2003, 2007). Most
of these models include finite mixture distributions at multiple levels of a hierarchical
structure. In the case of two levels, there are, for example, mixture distributions for
individuals (lower-level units) and for groups (higher-level units). In multivlevel
mixture models, selection of the number of mixture component is more complex
than in standard mixture models because one has to determine the number mixture
components at multiple levels.
The most popular measure for determining the number of mixture components
is the BIC. A problem in the application of this criterion in the context of multilevel
mixture models is that it contains the sample size as one of its terms. In multilevel
mixture models, it is not clear which sample size should be used in the BIC formula.
This could be the number of groups, the number of individuals, or either the number
of groups or number individuals depending on whether one wishes to determine the
number of components at the higher or at the lower level.
In this study we investigate the performance of various model selection methods
in the context of multilevel mixture models. We will not only look at BIC with difference
definitions of the sample sizes, but also at AIC, and AIC3, as well as at other
criteria such as ICOMP, validation log-likelihood, and LR tests with bootstrapped
p values.
Key words: Multilevel mixture models, Hierarchical models, BIC, AIC, AIC3
References
Vermunt, J.K.(2003): Multilevel latent class models. Sociological Methodology, 33,
213-239.
Vermunt, J.K. (2007): A hierarchical mixture model for clustering three-way data
sets. Computational Statistics and Data Analysis, 51, 5368-5376.
− 90 −

Exploring the Interaction Structure of Weblogs
Martin Klaus and Ralf Wagner
SVI Chair for International Direct Marketing
DMCC - Dialog Marketing Competence Center
University of Kassel, Germany
{mklaus,rwagner}@wirtschaft.uni-kassel.de
Abstract. Weblogs as a medium of the Web 2.0 have changed the way of communication
fundamentally but also created a new form of social interaction. Worldwide
users make up a huge, permanently growing conversation database including various
topics (Blood (2002)). An interesting feature of this virtual communication is
the opportunity of providing reference to other blogs by setting hyperlinks between
weblogs in the course of the dialog (Chin & Chignell (2006); Leskovec et al. (2007)).
Weblogs have no standardized document format and no tags indicate them.
Thus, it turns out to be challenging to identify and collect Weblogs from the web
with a crawler, spider, or bot (Anjewierden, Brussee & Efimova (2004)).
In this study we introduce different approaches to crawl weblogs and try to combine
them. Subsequently we use social network analysis to uncover the structure
between weblogs (Borgatti, Carley & Krackhardt (2006)). This structure provides
us with an assessment of the blogs and their relevance for marketing communication.
Key words: Marketing Communication, Social Network Analyzes, Web Mining,
Weblog
References
Anjewierden, A., Brussee, R., and Efimova, L. (2004): Shared Conceptualizations in
Weblogs. In: T.N. Burg (Ed.) BlogTalk 2.0, Vienna.
Blood, R. (2002): You‘ve Got Blog: How Weblogs are Changing our Culture. Perseus,
Cambridge.
Borgatti, S.P., Carley, K.M., and Krackhardt, D. (2006): Robustness of Centrality
Measures Under Conditions of Imperfect Data. Social Networks, 28, 234–236.
Chin, A. and Chignell, M. (2006): Finding Evidence of Community from Blogging
Co-citations: A Social Network Analytic Approach. In: Proceedings of 3rd
IADIS International Conference Web Based Communities 2006. San Sebastian,
Spain, 191–200.
Leskovec, J., McGlohon, M., Faloutsos, C., Glance, N.S., and Hurst, M. (2007):
Cascading Behavior in Large Blog Graphs. In: SDM ’07: SIAM Conference on
Data Mining.
− 91 −

ChIPmix : Mixture model of regressions for
ChIP-chip experiment analysis
Marie-Laure Martin-Magniette 1,2 and Tristan Mary-Huard 1 and Caroline
Bérard 1,2 and Stéphane Robin 1
1 UMR AgroParisTech/INRA MIA 518
2 URGV UMR INRA/CNRS/UEVE
Abstract. The Chromatin immunoprecipitation on chip (ChIP on chip) technology
is used to investigate proteins associated with DNA by hybridization to microarray.In
a two-color ChIP-chip experiment, two samples are compared: DNA fragments
crosslinked to a protein of interest (IP), and genomic DNA (Input). The two samples
are differentially labeled and then co-hybridized on a single array. The goal is
then to identify actual binding targets of the protein of interest, i.e. probes whose
IP intensity is significantly larger than the Input intensity.
We propose a new method called ChIPmix to analyse ChIP-chip data based on
mixture model of regressions. Let (xi, Yi) be the Input and IP intensities of probe i,
respectively. The (unknown) status of the probe is characterized through a label Zi
which is 1 if the probe is enriched and 0 if it is normal (not enriched). We assume
the Input-IP relationship to be:
Yi = a0 + b0xi + ɛi
= a1 + b1xi + ɛi
if Zi = 0 (normal)
if Zi = 1 (enriched)
where ɛi is a Gaussian random variable with mean 0 and variance σ 2 . The marginal
distribution of Yi for a given level of Input xi is
(1 − π)φ0(Yi|xi) + πφ1(Yi|xi), (1)
where π is the proportion of enriched probes, and φj(·|x) stands for the probability
density function of a Gaussian distribution with mean aj + bjx and variance σ 2 .
The mixture parameters (proportion, intercepts, slopes and variance) are estimated
using the EM algorithm. Posterior probabilities are used to classify probes into the
normal or enriched class. In the hypothesis test theory, the false discovery control
is performed by controlling the probability to reject wrongly the null hypothesis.
We propose an analogous concept in the mixture model framework. Our aim is to
control the probability for a probe to be wrongly assigned to the enriched class.
Therefore we control Pr{τi > s | xi, Zi = 0} = α for a predefined level α.
We present several applications of ChIPmix to promoter DNA methylation and
histone modification data and show that ChIPmix competes with classical methods
such as NimbleGen and ChIPOTle.
Key words: Classification, Mixture models, ChIP-chip
− 92 −

Clustering of High-Dimensional Data Via
Finite Mixture Models
Geoff McLachlan
Department of Mathematics & Institute for Molecular Bioscience
University of Queensland
Summary. There has been a proliferation of applications in which the number
of experimental units n is comparatively small but the underlying dimension p
is extremely large as, for example, in microarray-based genomics and other highthroughput
experimental approaches. Hence there has been increasing attention
given not only in bioinformatics and machine learning, but also in mainstream statistics,
to the analysis of complex data in this situation where n is small relative to p.
In this talk, we focus on the clustering of high-dimensional (continuous) data, using
normal mixture models. Their use in this context is not straightforward, as the normal
mixture model is a highly parameterized one with each component-covariance
matrix consisting of p(p + 1)/2 distinct parameters in the unrestricted case. Hence
some restrictions must be imposed and/or a variable selection method applied beforehand.
We shall review the existing literature and consider some new approaches
that have been proposed recently.
− 93 −

Majority-rule consensus: from preferences
(social choice) to trees (biology and
classification theory)
F.R. McMorris
Professor of Applied Mathematics
Professor of Computer Science
Illinois Institute of Technology
Chicago, IL 60616, USA
mcmorris@iit.edu
Abstract: The problem of aggregating the individual preferences of a group
of “voters” into a group consensus preference has been studied for many
years. Indeed, mathematical investigations of consensus problems go back
to the contributions of Borda (1784), of Condorcet (1785), and of Pareto
(1896) and are still frequently cited today. One method, the compelling
majority-rule consensus, is so simple (stick something in the output if it is in
more than half of the input) that it seems nothing really interesting can be
said about it. This presentation will give some historic background from the
classical preference case (e.g., voting), and then point out some new and old
mathematical and computational complexity results pertaining to the use of
the majority-rule paradigm for finding consensus phylogenetic trees (biology)
and classification structures (data analysis).
− 94 −

Optimization Methods with Evolutionary
Algorithms and Artificial Neurel Networks
Rene Meier and Franz Joos
Helmut-Schmidt-University
University of the Federal Armed Forces Hamburg
Power Engineering
Laboratory of Turbomachinery
Abstract. In order to optimize turbomachinery components it is necessary to
describe the behaviour of multimodal objective functions (OF). But it is timeconsuming
to evaluate the characteristics of these OF with a three-dimensional
Navier Stokes solver. Instead an Artificial Neural Network (ANN) is used as an
interpolator based on information contained in a database to correlate the performance
to the geometrical parameters as is done by a compressible three-dimensional
Reynolds-averaged Navier Stokes solver. With a computerized optimization system
an existing centrifugal impeller will be redesigned using an Evolutionary Algorithm
(EA) and an ANN. The ANN allows the evaluation of the OF for many geometries
generated by the EA with less effort than a Navier Stokes solver. Yet sometimes the
prediction is not accurate and must be verified by means of a more accurate but
time consuming Navier Stokes solver. The results of this verification are added to
the database. So a new optimization cycle is started with the expectation that the
new learning on a larger database will result in a more accurate ANN.
− 95 −

Finding Music Fads by clustering Online Radio
Data with Emergent Self-Organizing Maps
Florian Meyer and Alfred Ultsch
Databionics Research Group
University of Marburg, Germany
{meyer,ultsch}@informatik.uni-marburg.de
Abstract. Music charts provide a simple statistic of records sold. Due to web 2.0
and its social networks, detailed information from listeners is available. In particular,
there are user-generated keywords, so called tags, that group songs into genres. An
important topic for the music industry are music fads. I.e. small time intervals of few
weeks with a strong persistanc of similar music. A distance measure on weekly music
charts and tags is used. The sequenc of music charts is visualized using Emergent Self
Organizing Maps (ESOM). Fads are automatically found by clustering the charts
with the U*C clustering algorithm on ESOM. U*C does not need an estimation
of the number of clusters. Machine learned decision rules describe fads using the
dominant genres.
Key words: ESOM, U*C, Clustering, Tagged Music, Knowledge Representation
References
Ultsch, A. (2003): Maps for the Visualization of high dimensional Data Spaces.
Yamakawa T (Eds.):Proceedings of the 4th Workshop on Self-Organizing
Maps,225-230.
Mörchen, F., Ultsch, A., Nöcker, M., Stamm, C. (2005): Visual mining in music
collections In Proceedings 29th Annual Conference of the German Classification
Society (GfKl 2005), Magdeburg, Germany, Springer, Heidelberg
Lehwark, P., Risi,S. and Ultsch, A. (2007): Visualization and Clustering of Tagged
Music Data, Proceedings Workshop on Self-Organizing Maps (WSOM ’07),
Bielefeld, Germany,
Elias Pampalk (2001): Islands of Music Analysis, Organization, and Visualization
of Music Archives
Mörchen, F.et al. (2005): Databionic visualization of music collections according to
perceptual distance, Joshua D. Reiss, Geraint A. Wiggins (Eds), In Proceedings
6th International Conference on Music Information Retrieval (ISMIR 2005),
London, UK, pp. 396-403
Adamic, L. and E. Adar (2003), Friends and Neighbors on the Web, Social Networks,
25(3), 211–230.
− 96 −

Deviant box and dual clusters for the analysis
of conceptual contexts
Boris Mirkin
School of Computer Science and Information Systems
Birkbeck University of London, Malet street, London, WC1E 7HX, UK
mirkin@dcs.bbk.ac.uk
Summary. This work relates to the frameworks of biclustering (Madeira and
Oliveira 2004, Mirkin 1996) and formal concept analysis (Ganter and Wille 1999).
A formal concept over a 1/0 rectangular matrix r, whose row-set is I and columnset
is J, is a maximal pair (V, W ) such that V ⊂ I and W ⊂ J and all r-elements
within V × W are unities. The lattices of formal concepts found interesting applications
in such areas as association between itemsets and post-processing of web-search
results. However, in many applications the notion of formal concept seems overly
rigid because it does not allow any errors or peculiarities in 1/0 encoding (Pensa
and Boullicaut 2005). This is why we take the concept of data approximating box
V xW (Mirkin, Arabie and Hubert 1995) and use it in the framework of a disjunctive
biclustering model approximating the data matrix r.
We develop a method, Box(a), for fitting the model with possibly overlapping
boxes by using a local search algorithm for finding an optimal box starting from a
pre-specified row or column a and using a parameter b shifting the values of r to
r − b. It is proven that the method leads to highly deviant boxes, which is accounted
for by a variance measure.
We further proceed to develop a dual clustering framework by multiplying the
original model equation by its transposed version both on the right and on the
left. The two equations lead to disjunctive decompositions of similarity matrices,
(r − b) ∗ (r − b) ′ and (r − b) ′ ∗ (r − b) over clusters on row set I and column set
J, respectively. This dual clustering framework formalizes the notion that good
concepts should relate only such row and column sets that are similarity clusters
on their own. A local search method for simultaneously fitting the dual clustering
models, Dual(i, j), is developed using an evolutionary algorithm for optimization
the common intensity value of the clusters.
Results of experiments on generated and real data sets are reported supporting
the view of effectiveness of the algorithms.
Key words: Formal concept, Biclustering, Dual clustering, Scale shift
− 97 −

Clustering a Contingency Table Accompanied
by Visualization
Hans-Joachim Mucha
Weierstraß-Institut für Angewandte Analysis und Stochastik (WIAS),
D-10117 Berlin, Germany, mucha@wias-berlin.de
Abstract. Clustering techniques can be used for segmenting a heterogeneous twoway
contingency table into smaller, homogeneous parts. Following the paper of
Greenacre (1988), here the focus is on chi-square decompositions of the Person chisquare
statistic by clustering the rows and/or the columns of a contingency table. Especially
the hierarchical Ward method as well as a generalization of Ward’s method
will be considered. The latter can find clusters of different volume. Additionally, one
can show that it is also possible to carry out partitional cluster analysis by starting
from pairwise chi-square distances. Partitional clustering techniques optimize
some numerical criterion with respect to a fixed number of clusters K. Often the
partitional cluster analysis attains better solutions than hierarchical cluster analysis.
In any case, the correspondence analysis is the appropriate visualization tool for
both the contingency table and the clusters of the rows and/or columns. Moreover,
the correspondence analysis plots will become more informative by an additional
projection of a dendrogram. The latter shows the hierarchy of clusters. An application
from the field of ecology illustrates the segmentation of a contingency table
using different cluster analysis techniques.
Key words: chi-square distance, hierarchical clustering, partitional clustering, correspondence
analysis, dendrogram
References
Greenacre, M. J. (1988): Clustering the Rows and Columns of a Contingency Table.
Journal of Classification 5, 39–51.
− 98 −

Predictive classification trees
Ulrich Müller-Funk and Stephan Dlugosz
Institut für Wirtschaftsinformatik
University of Münster
Germany
Abstract. Tree-based algorithms for classification and regression are highly popular
because they give rise to results that are easy to interpret and to communicate.
(Some people argue, moreover, that factor selection comes along automatically. This
point, too, will be challenged in the paper.) CART and (exhaustive) CHAID figure
prominently among the procedures actually used in data based management etc.
CART is a well-established, nonlinear and nonparametric procedure that produces
binary trees. CHAID, in contrast, admits multiple splittings, a feature that allows to
exploit the splitting variable more extensively. On the other hand, that procedure
depends on premises that are questionable in practical applications. This can be
put down to the fact, that CHAID relies on simultaneous Chi-Square- resp. F-tests.
Both types of procedures – as implemented in SPSS, for instance – do not take into
account ordinal dependent variables. In the paper we suggest a tree-algorithm that
• requires categorical variables
• chooses splitting attributes by means of predictive measures of association,
• determines the cells to be united – and because of that the number of splits –
with the help of their conditional predictive power
• takes ordinal dependent variables into consideration
− 99 −

Efficient Media Exploitation towards Collective
Intelligence
Phivos Mylonas 1 , Vassilios Solachidis 2 , Andreas Geyer-Schulz 3 , Bettina
Hoser 3 , Sam Chapman 4 , Fabio Ciravegna 4 , Stefen Staab 5 ,PavelSmrz 6 ,
Yiannis Kompatsiaris 2 , and Yannis Avrithis 1
1 National Technical University of Athens, Image, Video and Multimedia Systems
Laboratory, Iroon Polytechneiou 9, Zographou Campus, Athens, GR 157 80,
Greece,{fmylonas, iavr}@image.ntua.gr
2 Centre of Research and Technology Hellas, Informatics and Telematics Institute,
1st Km Thermi-Panorama Road, Thermi-Thessaloniki, GR 570 01, Greece,
{vsol, ikom}@iti.gr
3 Department of Economics and Business Engineering, Information Service and
Electronic Markets, Kaiserstraße 12, Karlsruhe 76128, Germany
{andreas.geyer-schulz, bettina.hoser}@kit.edu
4 University of Sheffield, Department of Computer Science, Regent Court, 211
Portobello Street, S1 4DP, Sheffield, UK {s.chapman, fabio}@dcs.shef.ac.uk
5 Universität Koblenz-Landau, Information Systems and Semantic Web,
Universitätsstraße 1, 57070 Koblenz, Germany, staab@uni-koblenz.de
6 Brno University of Technology, Faculty of Information Technology, Bozetechova
2, CZ-61266 Brno, Czech Republic smrz@fit.vutbr.cz
Abstract. In this work we propose intelligent, automated content analysis techniques
for different media to extract knowledge from the multimedia content. Information
derived from different sources/modalities will be analyzed and fused, in
terms of spatiotemporal, personal and even social contextual information. In order
to achieve this goal, semantic analysis will be applied to the content items, taking
into account the content itself (e.g. text, images and video), as well as existing
personal, social and contextual information (e.g. semantic and machine-processable
metadata and tags). The above process exploits the so-called “Media Intelligence”
towards the ultimate goal of identifying “Collective Intelligence”, emerging from
the collaboration and competition among people, empowering innovative services
and user interactions. The utilization of “Media Intelligence” constitutes a departure
from traditional methods for information sharing, since semantic multimedia
analysis has to fuse information from both the content itself and the social context,
while at the same time the social dynamics have to be taken into account. Such
intelligence provides added-value to the available multimedia content and renders
existing procedures and research efforts more efficient.
− 100 −

Support Vector Machines in the Dual using
Majorization and Kernels
Georgi Nalbantov 1 , Patrick J.F. Groenen 2 , and Cor Bioch 3
1 MICC, Maastricht University and
Econometric Institute, Erasmus University Rotterdam, The Netherlands
nalbantov@few.eur.nl
2 groenen@few.eur.nl
3 bioch@few.eur.nl
Abstract. Recently, Support Vector Machines (SVMs) have proved to be a quite
successful method for classification. One of the bottlenecks with this approach from a
practical point of view is that existing solvers are rather slow in speed. Usually, SVM
solvers use specialized iterative optimization algorithms to solve the SVM optimization
problem that are quite slow, especially in the so-called dual SVM formulation.
Here, we propose to use another iterative method, which is a majorization method.
It has already been applied successfully for solving the primal SVM formulation (see,
Groenen, Nalbantov, and Bioch, 2007, 2008). The contribution of this paper is to
extend it to the dual formulation. This opens the door for, first of all, using different
so-called kernel functions, which allow for nonlinear decision functions, and second,
for handling more efficiently linear problems where the number of input variables is
bigger than the number of observations.
Key words: Support vector machines, Iterative majorization, Binary classification
problem, Kernels
References
Groenen, P.J.F., Nalbantov, G., and Bioch, J.C. (2007): Nonlinear support vector
machines through iterative majorization and I-splines. In: R.Decker, H-.J. Lenz
(Eds.): Advances in data analysis. Springer, Berlin, 149–162.
Groenen, P.J.F., Nalbantov, G., and Bioch, J.C. (2008, in press): SVM-Maj: A
Majorization Approach to Linear Support Vector Machines with Different Hinge
Errors. Advances in Data Analysis and Classification.
− 101 −

Approach for Dynamic Problems in Clustering
Anneke Neumann, Klaus Ambrosi, and Felix Hahne
Institut für Betriebswirtschaft und Wirtschaftsinformatik
Stiftung Universität Hildesheim
{aneumann,ambrosi,hahne}@bwl.uni-hildesheim.de
Abstract. In cluster analysis, a variety of methods has been developed for different
areas of application (e.g. economics, biology, medicine, psychology), some of
which were implemented in data evaluation software packages (e.g. SPSS x , SAS). In
many scenarios, particularly economic ones, special methods are required in order
to analyze the development of clusters over time. While there are such methodical
extensions for factor analysis and multidimensional scaling, hardly any dynamic
approaches exist in the field of cluster analysis.
In this talk, special attention will be paid to dynamic fuzzy clustering problems.
Known approaches will be reviewed critically concerning their applicability in dynamic
problems, and a new fuzzy clustering approach will be introduced which can
be applied to dynamic problems.
Key words: Clustering, Fuzzy Clustering, Dynamic Data Analysis
References
Basford, K.E. and McLachlan, G.J. (1985): The Mixture Method of Clustering Applied
to Three-Way Data. Journal of Classification, 2, 109–125.
Höppner, F., Klawonn, F., Kruse, R., and Runkler, T. (1999): Fuzzy Cluster Analysis.
Wiley, Chichester, New York.
Joentgen, A., Mikenina, L., Weber, B., and Zimmermann, H.-J. (1999): Dynamic
fuzzy data analysis based on similarity between functions. Fuzzy Sets and Systems,
105, 81–90.
Tucker, L.R. (1966): Some mathematical notes on three-mode factor analysis. Psychometrika,
31, 279–311.
− 102 −

Robust fitting of mixtures: The approach based
on the Trimmed Likelihood Estimator
Neyko Neykov 1 , Peter Filzmoser 2 , and Plamen Neytchev 1
1 Department of Statistics and Probability Theory, Vienna University of
Technology, Austria P.Filzmoser@tuwien.ac.at
2 National Institute of Meteorology and Hydrology, Bulgarian Academy of
Sciences, Sofia, Bulgaria {Neyko.Neykov}{Plamen.Neytchev}@meteo.bg
Abstract. The Maximum Likelihood Estimator (MLE) has commonly been used to
estimate the unknown parameters in a finite mixture of distributions. However, the
MLE can be very sensitive to outliers in the data. In order to overcome this problem,
Neykov et al. (2007) adapted the trimmed likelihood methodology developed by
Vandev and Neykov (1998) and Neykov and Müller (2003) to estimate mixtures
in a robust way. The superiority of this approach in comparison with the MLE
is illustrated by examples and simulation studies. The behavior of the widely used
classical criteria for the assessment of the number of components in a mixture model
and their robustified versions are also studied in the presence of outliers.
Key words: Trimmed likelihood estimator, Finite mixtures of distributions
References
Maronna, R., Martin, R.D. and Yohai, V.J. (2006): Robust Statistics: Theory and
Methods. Wiley, New York.
McLachlan, G.J. and Peel, D. (2000): Finite Mixture Models. Wiley, New York.
Neykov, N. and Müller, C. (2003): Breakdown Point and Computation of Trimmed
Likelihood Estimators in GLMs. In: R. Dutter et al., (eds), Developments in
robust statistics, pp. 277–286, Physica Verlag, Heidelberg.
Neykov, N.M., Filzmoser, P., Dimova, R. and Neytchev, P.N. (2004): Mixture of
Generalized Linear Models and the Trimmed Likelihood Methodology. In: J.
Antoch (Ed.): Proceedings in Computational Statistics. Physica-Verlag, Heidelberg,
1585–1592.
Neykov, N., Filzmoser, P., Dimova, R. and Neytchev, P. (2007): Robust Fitting of
Mixtures Using the Trimmed Likelihood Estimator. Computational Statistics &
Data Analysis, 17(3), 299–308.
Vandev, D.L. and Neykov, N.M. (1998): About Regression Estimators with High
Breakdown Point. Statistics, 32, 111–129.
− 103 −

Cluster Tree Estimation using a Generalized
Single Linkage Method
Rebecca Nugent 1 and Werner Stuetzle 2
1 Department of Statistics, Carnegie Mellon University, Baker Hall, Pittsburgh,
PA 15213, USA. rnugent@stat.cmu.edu
2 Department of Statistics, University of Washington, Box 354322, Seattle, WA
98195, USA wxs@stat.washington.edu
Abstract. The goal of clustering is to detect the presence of distinct groups in a
data set and assign group labels to the observations. In nonparametric clustering,
we regard the observations as a sample from an underlying density and assume that
groups correspond to modes of this density. The goal then is to find the modes
and assign each observation to the domain of attraction of a mode. The (possibly
hierarchical) modal structure of a density is summarized by its cluster tree; modes
of the density correspond to leaves in the cluster tree. Estimating this cluster tree
is the fundamental goal of nonparametric cluster analysis.
We adopt a plug-in approach: estimate the cluster tree of the underlying density
by the cluster tree of a density estimate. For density estimates that are piecewise
constant (and so have computationally tractable level sets), the cluster tree can
be computed exactly. However, for other density estimates, particularly in highdimensions,
we have to be content with an approximation. We present a graph-based
method that approximates the cluster tree for any density estimate and includes
the introduction of a density-based similarity measure between observations. After
motivating the method, we show results that allow us to reduce the graph to a
spanning tree and then sketch an algorithm that allows the exact computation of the
spanning tree whose edge weights are not of closed form. We point out mathematical
and algorithmic similarities to single linkage clustering and illustrate our approach
on several examples.
Key words: cluster analysis, single linkage clustering, level sets, minimum density
similarity measure, nearest neighbor density estimation
References
Stuetzle, W. and Nugent, R. (2007): A generalized single linkage method for estimating
the cluster tree of a density. Technical Report 514, Department of Statistics,
Univeristy of Washington.
− 104 −

Multi-Class Extension of Verifiable Ensemble
Models for Safety-Related Applications
Sebastian Nusser 1,2 , Clemens Otte 1 , and Werner Hauptmann 1
1 Siemens AG, Corporate Technology, Otto-Hahn-Ring 6, 81730 Munich, Germany,
{sebastian.nusser.ext,clemens.otte,werner.hauptmann}@siemens.com
2 School of Computer Science, Otto-von-Guericke-University of Magdeburg,
Universitätsplatz 2, 39106 Magdeburg, Germany
Abstract. For safety-related applications, models learned from data must be verifiable
and, thus, interpretable by domain experts. In a previous work (Nusser et al.,
2007) we developed a sequential covering algorithm for binary classification problems
in safety-related domains. It is based on ensembles of low-dimensional submodels,
where each submodel as well as the overall ensemble model can be verified. Thus, the
correct interpolation and extrapolation behavior of the complete model can be guaranteed.
In the present contribution we extend the approach to multi-class problems.
The extension is not straight-forward since common methods like one-against-one or
one-against-rest voting (Friedman, 1996; Hsu and Lin, 2002) may introduce inconsistencies.
We show that inconsistencies can be avoided by introducing a hierarchy
of misclassification costs. Such hierarchy is used to define a strict ordering of the
kind: “class c1 should never be misclassified, class c2 might only be misclassified as
c1, class c3 might be misclassified as c1 or c2.” Our method follows a sequential
covering concept also for multi-class classification: low-dimensional submodels are
trained to separate the samples of the class with the minimal misclassification costs
from the samples of all remaining classes. If the problem is solved for this class or
no further improvements are possible, all remaining samples of this class are removed
from the training data set and the procedure is repeated for the next class
within the hierarchy of misclassification costs. Experimental evaluation carried out
on benchmark data sets from the UCI Machine Learning Repository shows a good
trade-off between interpretation and prediction accuracy of our method.
Key words: multi-class, ensemble learning, local modeling, interpretability
References
Friedman, J. H. (1996). Another approach to polychotomous classification. Technical
report, Department of Statistics, Stanford University.
Hsu, C.-W. and Lin, C.-J. (2002). A comparison of methods for multiclass support
vector machines. Neural Networks, IEEE Trans. on, 13(2):415–425.
Nusser, S., Otte, C., and Hauptmann, W. (2007). Learning binary classifiers for
applications in safety-related domains. In Proceedings of 17th Workshop Computational
Intelligence, pages 139–151. Universitätsverlag Karlsruhe.
− 105 −

Analysis of Borrowing and Guaranteeing
Relationhships among Government Officials at
the Eighth Century in the Old Capital of Japan
Akinori Okada 1 and Towao Sakaehara 2
1
Graduate School of Management and Information Sciences, Tama University
4-1-4 Hijirigaoka, Tama-shi, Tokyo 206-0022 Japan okada@tama.ac.jp
2
Department of History, Faculty and Graduate School of Literature and Human
Sciences, Osaka City University
Sugimoto-cho 3 Sumiyoshi-ku Osaka City 558-8585 Japan
sakaehar@lit.osaka-cu.ac.jp
Abstract. In the present study relationships among lower ranked government officials,
working in the old capital of Japan called Heijo-kyo at the eighth century, are
analyzed. They were engaged in copying the Buddhist sutra in the capital. The documents
which show the borrowing and guaranteeing relationships among them have
been kept in the governmental warehouse called Shoso-in (Sakaehara, 1987). The
documents tell (a) the borrower, (b) the amount of money borrowed, (c) who stood
guarantee for the borrower, (d) the date of borrowing. From these documents, the
table which shows the borrowing and guaranteeing relationships among government
officials was derived. The (j, k) element of the table shows the amount of money
the government official corresponding to row j borrowed which was guaranteed by
the government official corresponding to column k. One who stood guarantee for
his colleague seem more dominant than one who borrowed. These relationships are
asymmetric. The table was derived for the years 772, 773, and 774 (including the
beginning of 775). The table was analyzed by the asymmetric multidimensional scaling
(Okada and Imaizumi, 1997). The obtained configuration shows the dominance
relationships among government officials and groups of them.
Key words: Asymmetry, Borrowing and guaranteeing relationships, Historical
data, Multidimensional scaling
References
Okada, A. and Imaizumi, T. (1997): Asymmetric multidimensional scaling of twomode
three-way proximities. Journal of Classification, 14, 195–224.
Sakaehara,T. (1987): People’s Life Styles in the Capital City. In: T. Kishi, A.
(Ed.): Modes of Life in the Capital Cities. Chuokoron-sha, Tokyo, 187–266.
(in Japanese)
− 106 −

Variable Selection for kernel classifiers:
A Feature-to-Input Space Approach
Surette Oosthuizen and Sarel Steel
Department of Statistics and Actuarial Science, University of Stellenbosch, Private
Bag X1, 7602 Matieland, South Africa (surette@sun.ac.za; sjst@sun.ac.za)
Abstract. Consider using values of input variables X1, X2, · · · , Xp to classify entities
into one of two groups. Kernel classifiers, e.g. support vector machines (SVMs)
and kernel Fisher discriminant analysis (KFDA), are known to be exceptionally well
suited for this task. In general the classification accuracy of SVMs and KFDA can
however be improved substantially if instead of the comprehensive set of p input
variables, a smaller subset of (say m) input variables is used. Let the space in which
the training patterns reside, be called the input space. Also, let Φ map the input
space to a higher-dimensional so-called feature space. An aspect which complicates
variable selection for non-linear kernel classifiers is that they make implicit use of
Φ: they are linear functions in a higher-dimensional feature space. Since Φ is usually
unknown, and the feature space can be infinite-dimensional, the implicit transformation
step obscures the contributions of variables to the kernel discriminant function.
In this paper we propose a new variable selection approach for kernel classifiers,
viz. so-called feature-to-input space (F I) selection. The basic idea underlying this
approach is to combine the information obtained from feature space with the easy
interpretation in input space. We discuss several approaches and evaluate the resulting
selection criteria in a fairly extensive simulation study.
Key words: Variable Selection, Kernel Based Classification, Kernel Methods
References
Mika, S., Rätsch, G., Weston, J., Schölkopf, B., Smola, A.J. and Müller, K.-R. (1999).
Fisher discriminant analysis with kernels. Proceedings of Neural Networks for
Signal Processing, 9, 41-48.
Shawe-Taylor, J. and Cristianini, N. (2004). Kernel methods for pattern analysis.
Cambridge University Press, Cambridge.
Rakotomamonjy, A. (2002). Variable selection using SVM-based criteria. Perception
Systeme Information, Insa de Rouen, Technical Report PSI 2002-04.
Rakotomamonjy, A. (2003). Variable selection using SVM-based criteria. Journal of
Machine Learning Research, 3, 1357-1370.
− 107 −

Classifying hospitals with respect to their
diagnostic diversity using Shannon’s entropy
Thomas Ostermann 1 , Reinhard Schuster 2 , and Christoph Erben 2
1 Department of Medical Theory and Complementary Medicine, University of
Witten/Herdecke, Gerhard-Kienle-Weg 4, 58313 Herdecke, Germany,
thomaso@uni-wh.de
2 Medical Review Board of the Statutory Health Insurance in North Germany,
Katharinenstr 11a, 23554 Luebeck, Germany, Reinhard.Schuster@mdk-nord.de
Abstract. Background: In Germany hospital comparisons are part of health status
reporting. However, some methodological problems arise in classifying hospitals
by means of their reported data. This article presents the application of Shannon’s
entropy measure for hospital comparisons. Material and Methods: We used Shannon’s
entropy given by E(p1, . . . , pn) = Pn k=1 pk log pk as an approach to measure
the diagnostic diversity of a hospital department. Based on a data set of aggregated
three–digit ICD–9–codes from the L4–hospital statistics of 1998 in Schleswig Holstein
we compared the resulting measures for diagnostic diversity with respect to
the hospital departments area (e.g. surgery, gynecology) and to the hospital status
(primary, secondary, tertiary or specialized hospital). Results: Highly specialized
departments like obstetrics (0.44) or ophthalmology (0.46) do generate lower entropy
values than area-spanning departments like radiology (0.52) or general gynaecology
(0.56), which have significantly higher values. Discussion: We showed how entropy
can be used as a measure for classifying hospitals. Our approach can basically be
implemented in all fields of health services research, where categorial data emerges.
Especially in DRG-data this approach is quite promising and should be applied.
Key words: Entropy, diagnostic diversity, hospital comparison, classification
References
Brindle, G.W. and Gibson, C.J. (2007): Entropy as a measure of diversity in an
inventory of medical devices. Medical Engineering & Physics,29, Epub.
Elayat, H.A., Murphy, B.B. and Prabhakar, N.D. (1978): Entropy in the hierarchical
cluster analysis of hospitals. Health Serv Res, 13, 395–403.
Erben, C.M. (2000): The concept of entropy as a possibility for gathering mass data
for nominal scaled data in heath status reporting. Stud Health Technol Inform,
77, 118–9.
− 108 −

Clustering and Dimensionality Reduction to
Discover Interesting Patterns in Binary Data
Francesco Palumbo
Dipartimento di Istituzioni Economiche e Finanziarie - University of Macerata
Via Crescimbeni, 20 - I-62100, Italy
francesco.palumbo@unimc.it
Abstract. A key element in the success of data analysis is the strong contribution
of visualization: dendrograms and factorial plans are intuitive ways to display
association relationships within and among sets of variables and groups of units.
In the Association Rules (AR) mining we refer to a n × p data matrix, where n
indicates the number of statistical units and p the number of attributes, which are
also called items. The problem consists in analyzing links between attributes. Sets
of attributes that co-occur through the whole data matrix are referred as patterns.
Scanning the whole data set and analyzing all the relationships is an interesting
and promising approach, yet this approach leads to a NP -hard problem and gets
no solution when dealing with a large number of attributes.
Moreover, in some cases, the most interesting relationships refer to subpopulations
in the data, and they are hidden by the obvious ones and cannot be identified
by the classical descriptive and inferential statistical methods.
The joint use of factorial and clustering methods in a unitary exploratory approach
copes with these issues. It allows the analyst to identify the most interesting
groups of units and sets of attributes; by focusing the attention only on them more
easily interesting patterns are identified in large and huge binary data base. ns in
large and huge binary data base, focusing the attention only on them.
Key words: Dimensionality Reduction, Binary Data, AR mining
References
Iodice D’Enza A., Palumbo F. and Greenacre M. (2007): Exploratory data analysis
leading towards the most interesting simple association rules. Comput. Statist.
Data Anal., Corrected Proof, doi:10.1016/j.csda.2007.10.006.
Mizuta, M. (2004): Dimension reduction methods. In J.E. Gentle, W. Hardle and
Y. Mori (Eds.): Handbooks of Computational Statistics. Concepts and Methods.
Springer-Verlag, Heidelberg, pp. 565-589.
Plasse M., Niang N., Saporta G., Villeminot A. and Leblond L. (2007): Combined
use of association rules mining and clustering methods to find relevant links
between binary rare attributes in a large data set. Comput. Statist. Data Anal.,
doi: 10.1016/j.csda.2007.02.020.
− 109 −

Lineare Kodierung multipler Vererbungshierarchien:
Wiederbelebung einer antiken Klassifikationsmethode
Wiebke Petersen
Institut für Sprache und Information, Heinrich-Heine-Universität Düsseldorf,
petersew@uni-duesseldorf.de
Zusammenfassung. Die in der mehr als zweitausend Jahre alten Sanskritgrammatik
von Pān. ini eingesetzten formalen Methoden erstaunen ob ihrer Modernität.
Der Vortrag widmet sich insbesondere der Methode zur Repräsentation von Mengen
als Intervalle einer Liste, die zur Klassifikation der Lautklassen eingesetzt wird.
Diese Methode zeichnet sich dadurch aus, daß sie es erlaubt, bestimmte Polyhierarchien
(d.h., Hierarchien in denen eine Klasse mehr als eine direkte Oberklasse
haben kann) linear zu kodieren. Monohierarchien lassen sich als verschachtelte Listen
linear repräsentieren, da sie immer eine Baumstruktur bilden. Für allgemeine
Polyhierarchien steht eine lineare Repräsentationsmethode noch aus; sie werden häufig
mithilfe einer Menge von Constraints beschrieben, wobei die Hierarchie zumeist
in die einzelnen Elemente ihrer binären Nachbarschaftsrelation zerlegt wird. Infolge
davon müssen viele Anfragen, zum Beispiel nach einer hierarchischen Teilstruktur,
umständlich über rekursive Aufrufe abgearbeitet werden. Ein weiterer Nachteil von
Polyhierarchien besteht darin, daß sie häufig aufgrund zahlreicher Kantenkreuzungen
schwer lesbar sind. Da kreuzungsfreie Hierarchien von den Nutzern eines Systems
besser akzeptiert und verstanden werden, schließen viele aktuelle Ontologiesysteme
Polyhierarchien aus, oder verbergen sie zumindest vor den Anwendern. Aus diesen
Gründen lassen zahlreiche Formalismen nur baumförmige Hierarchien zu.
In dem Vortrag soll zunächst eine vollständige Charakterisierung der Klassifikationen
gegeben werden, deren Klassen sich gemäß Pān. inis Methode als Intervalle
einer Liste darstellen lassen. Eine solche Klassifikation wird S-darstellbar genannt. Es
wird desweiteren formal gezeigt, daß sich die Hasse-Diagramme S-darstellbarer Klassifikationen
immer kreuzungsfrei zeichnen lassen. Schließlich soll untersucht werden,
inwieweit es angebracht ist, für bestimmte Einsatzzwecke die Klasse der zulässigen
Hierarchien von baumförmigen auf S-darstellbare auszudehnen, um einerseits
zumindest eingeschränkt multiple Vererbung zuzulassen, ohne andererseits die Vorteile
einer kreuzungsfreien Zeichnung und einer effizienten linearen Kodierung und
Verarbeitung hierarchischer Beziehungen zu verlieren.
Schlüsselwörter: Pān. ini, Hierarchien, kreuzungsfreie Zeichnung, lineare Kodierung
− 110 −

Begriffsanalytischer Ansatz zur qualitativen
Zitationsanalyse
Wiebke Petersen und Petja Heinrich
Institut für Sprache und Information, Heinrich-Heine-Universität Düsseldorf,
petersew@uni-duesseldorf.de
Zusammenfassung. Zu den Aufgaben der Bibliometrie gehört die Zitationsanalyse
(Kessler 1963), das heißt die Analyse von Kozitationen (zwei Texte werden kozitiert,
wenn es einen Text gibt, in dem beide zitiert werden) und die bibliographische
Kopplung (zwei Texte sind bibilographisch gekoppelt, wenn beide eine gemeinsame
Zitation aufweisen).
In dem Vortrag wird aufgezeigt werden, daß die Formale Begriffsanalyse (FBA)
für eine qualitative Zitationsanalyse geeignete Mittel bereithält. Eine besondere Eigenschaft
der FBA ist, daß sie die Kombination verschiedenartiger (qualitativer und
skalarer) Merkmale ermöglicht. Durch den Einsatz geeigneter Skalen kann auch dem
Problem begegnet werden, daß die große Zahl von zu analysierenden Texten bei qualitativen
Analyseansätzen in der Regel zu unübersichtlichen Zitationsgraphen führt,
deren Inhalt nicht erfaßt werden kann.
Die Relation der bibliographischen Kopplung ist eng verwandt mit den von Priss
entwickelten Nachbarschaftskontexten, die zur Analyse von Lexika eingesetzt werden.
Anhand einiger Beispielanalysen werden die wichtigsten Begriffe der Zitationsanalyse
in formalen Kontexten und Begriffsverbänden modelliert. Es stellt sich
heraus, daß die hierarchischen Begriffsverbände der FBA den gewöhnlichen Zitationsgraphen
in vielerlei Hinsicht überlegen sind, da sie durch ihre hierarchische
Verbandstruktur bestimmte Regularitäten explizit erfassen. Außerdem wird gezeigt,
wie durch die Kombination geeigneter Merkmale (Doktorvater, Institut, Fachbereich,
Zitationshäufigkeit, Keywords) und Skalen häufigen Fehlerquellen wie Gefälligkeitszitationen,
Gewohnheitszitationen u.s.w. begegnet werden kann.
Schlüsselwörter: Bibliographische Kopplung, Kozitation, Formale Begriffsanalyse
Literaturverzeichnis
B. Ganter & R. Wille (1999): Formal Concept Analysis. Mathematical Foundations.
Berlin: Springer.
M.M. Kessler (1963): Bibliographic coupling between sientific papers. American Documentation,
Vol. 14, 10–25.
U. Priss & J. Old (2004): Modelling Lexical Databases with Formal Concept Analysis.
Journal of Universal Computer Science, Vol. 10, Nr. 8, 967–984.
− 111 −

The Analysis of the power for some chosen VaR
backtesting procedures - simulation approach
Krzysztof Piontek
Department of Financial Investments and Risk Management
Wroclaw University of Economics
ul. Komandorska 118/120, 53-345 Wroclaw, Poland
krzysztof.piontek@ae.wroc.pl
Abstract. The definition of Value at Risk is quite general. There are many approaches
which can give different VaR values. The challenge is not to suggest a new
method but to distinguish between good and bad models. Backtesting is the necessary
statistical procedure to evaluate performance of VaR models and select the
best one. If the power of the test is low, then it is likely to mis-classify an inaccurate
VaR model as well-specified. It can be a threat to financial institutions.
The aim of this article is to analyze backtesting methodologies, focusing on the
aspect of limited data set and the power of tests. There are three groups of methods
for validating VaR models: based on the frequency of failures, based on various
loss functions and the ones based on the adherence of a VaR model to asset return
distributions. This article presents and summarizes some of frequently used methods
from every group (proposed by Kupiec, Christoffersen, Lopez and Berkowitz).
However, the main part of this work is statistical evaluation of the most applied
tests for small data sets (usually observed in practice). We analyze performance
of tests based on the type II error, in order to select the best one for different
numbers of observations and model mis-specifications. For making this verification
asset return simulations are used. Presented results indicate that some tests are not
adequate for small samples, even for 1000 observations, which is a very important
issue if acceptance of internal models for market risk management is considered.
Key words: risk measurement, Value at Risk, backtesting, power of tests
References
Hass, M. (2001): New Methods in Backtesting, CAESAR,
www.caesar.de/uploads/media/cae pp 0010 haas 2002-02-05 01.pdf
Piontek, K. (2007): A Survey and a Comparison of Backtesting Procedures
(in Polish), In: P. Chrzan: Metody matematyczne, ekonometryczne..., Katowice.
Sarma, M., Thomas, S., Shah, A. (2003): Selection of Value-at-Risk Models,
ideas.repec.org/s/jof/jforec.html
− 112 −

Testing distribution in errors in variables
models
Denys Pommeret 1
Aix-Marseille 2 University pommeret@iml.univ-mrs.fr
Abstract. Within the frame of errors in variables models we consider the sum of
two independent random variables, X = W + Z, where W is the variable of interest
with known distribution Π, and where the error Z has unknown density f. We
present a smooth goodness of fit test for testing the distribution of the error Z. For
that we observe an i.d.d. sample X1, · · · , Xn, with a mixture density function
Z
g(x) = f(x, m)Π(dm),
where Π is a real probability distribution and f(x, m) are real m-parameterized
density functions, for m in some set M ⊂ R. We assume that Π is the known
distribution of Z and we want to test
H0 : f(x, m) = f0(x, m), for all m in M,
where f0 is a specified probability density function. An adaptation of the Neyman
smooth test is proposed.
Key words: Mixture models, Neyman’s test, Score statistic, Schwarz’s criteria
References
Hart, J.D. (1997): Nonparametric smoothing and lack-of-fit tests, Springer Series in
Statistics. New York, NY.
Kallenberg, W.C.M. and Ledwina, T. (1995): Consistency and Monte Carlo simulation
of data driven version of smooth goodness of fit tests, Ann. Statist, 23,
1594–1608.
Ledwina, T. (1994): Data-Driven Version of Neyman’s Smooth Test of Fit, Journal
of the American Statistical Society, 89, 1000–05.
Lehmann, E.L. and Romano, J.P. (2005): Testing statistical hypotheses, 3rd ed.
Springer Texts in Statistics. New York, NY: Springer.
− 113 −

Classification with an increasing number of
components
Odile Pons 1
INRA, Mathematics, Jouy-en-Josas, France Odile.Pons@jouy.inra.fr
Abstract. After estimating the rn actual components of a mixture with an increasing
number of components increasing with the sample size, the question is to
determine to which group a given observation Xi, i = 1, . . . , n, belongs. A classification
consists in mapping an observation Xi (or a value x of X ) into a class � kn(Xi)
(or � kn(x)) in {1, . . . , rn} which may either be uniquely defined (fixed case) or related
to a random distribution. In both cases, the component � kn is chosen by maximum
likelihood with a penalization.
A random classification avoids misclassification of some observations with overlapping
densities, k(Xi) = j with an estimated probability and k(x) = j with a fixed
probability, 1 ≤ j ≤ rn. They are estimated by
�µ �
n, kn(x) � fn,
kn(x) � = max Qn(�µn,k
1≤k≤rn
� fn,k; x), with
Qn(�µn,k � fn,k; x) = �µn,k � fn,k(x) − nλ 2 n
−ν 2 n
�
1≤j≤r
q�
j=1
π( � fn,k − � fn,j),
p (�µn,j)
where the penalization coefficients λn and νn tend to zero as n → ∞, π and λ
are smooth functions. For a random classification, � kn(Xi) is defined in the same
way with some probabilities. The random procedure preserves all the classes: the
proportions of observations belonging to {1, . . . , rn} is asymptotically identical to
the mixture probabilities.
Key words: Mixture, classification,asymptotics
References
Lemdani, M. and Pons, O. Large Mixture models with an increasing number of
components, unpublished, (2007).
Pons, O. Asymptotic distributions in finite semi-parametric mixture models. to appear,
(2008).
− 114 −

Bagging with different split criteria.
Sergej Potapov and Berthold Lausen
Department of Biometry and Epidemiology, Friedrich-Alexander-University
Erlangen-Nuremberg, Waldstraße 6, D-91054 Erlangen, Germany
Sergej.Potapov@imbe.imed.uni-erlangen.de
Abstract. In recent years many papers discuss boosting and bagging based methods
for supervised learning or machine learning. Both concepts aggregate sets of
estimated trees, which are derived by split criteria without adjusting for variables
measured on different scales. Breiman et al. (1984) observed that quantitative variables
tend to be more often selected as binary variables. As a solution Lausen et al.
(1994, 2004) introduced p-value adjusted classification and regression trees, which
introduce the p-value of maximally selected test statistics as split criteria. The p
value adjustment avoids the possible selection bias of variables measured on different
scales. The R package TWIX of Potapov et al. (2008) offer p-value adjusted
classification trees and bagging of p-value adjusted classification trees. In our paper
we compare bagging, double-bagging (Hothorn and Lausen, 2003) without and with
p-value adjustment by means of simulation. Moreover, we illustrate our approach
using a clinical study involving micro array data.
Key words: bagging, CART, machine learning, trees, micro array data
References
Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J. (1984): Classification and
regression trees. Wadsworth Press.
Hothorn, T., Lausen, B. (2003): Double-bagging: Combinig classifiers by bootstrap
aggregation. Pattern Recognition 36(6), 1303–1309.
Lausen, B., Hothorn, T., Bretz, F., Schumacher, M. (2004): Assessment of optimal
selected prognostic factors.Biometrical Journal 46, 364–374.
Lausen, B., Sauerbrei, W., Schumacher, M. (1994): Classification and regression trees
(CART) used for the exploration of prognostic factors measured on different
scales, in: Dirschedl, P., and Ostermann, R. (eds.), Computational Statistics,
Physica-Verlag, Heidelberg, 483–496.
Potapov, S., Theus, M. (2008): The TWIX package (Version 0.2.4.). http://cran.rproject.org
− 115 −

Remarks on the Existence of CML Estimates
for the PCM by means of the R Package eRm
Antonio Punzo 1
University of Milano-Bicocca, Department of Quantitative Methods for Business
and Economic Sciences, a punzo@libero.it
Abstract. Mair and Hatzinger (2007) have recently proposed in the Journal of
Statistical Software the R package eRm (extended Rasch models) for computing
Rasch models and several extensions. Undoubtedly, in the eRm class the partial
credit model (PCM) – for practical testing purposes – is one of the best known.
The package, using a unitary conditional maximum likelihood (CML) procedure,
estimates the item parameters of the above-mentioned models.
Although the eRm belong to the Rasch family of models and share their distinguishing
characteristics, they suffer from the problem of possible non-existence
of estimates. In literature, both in the joint and in the conditional ML approach,
the configurations and the conditions of non-existence for the RM are well-known
(Fischer, 1981). The eRm package performs a preliminary data check only for the
RM. The conditions of non-existence are known for the PCM only in the joint case
(Bertoli-Barsotti, 2005).
In this article, the main focus is on the PCM; the above-mentioned JML nonexistence
configurations for this model will be the starting point. A class of counter
examples is illustrated, which leads to “false” CML estimates with the eRm package,
i.e., values that appear to be estimates but, through a more accurate analysis
of the maximization function, they are rather a clear signal of non-existence. Moreover,
the obtained results emphasize the presence of additional CML non-existence
configurations, compared to those valid in the JML case.
Key words: Rasch models, Partial Credit model, Conditional Maximum Likelihood
estimate, R package eRm
References
Bertoli-Barsotti, L. (2005): On the Existence and Uniqueness of JML Estimates for
the Partial Credit Model. Psychometrika, 70, 3, 517–531.
Fischer, G. H. (1981): On the Existence and Uniqueness of Maximum-Likelihood
Estimates in the Rasch Model. Psychometrika, 46, 1, 59–77.
Mair P. and Hatzinger R. (2007): Extended Rasch Modeling: The eRm Package for
the Application of IRT Models in R. Journal of Statistical Software, 20, 9, 1–20.
− 116 −

Dynamic disturbances in BTA deephole
drilling - Identification of spiralling as a
regenerative effect
Nils Raabe, Dirk Enk, Claus Weihs, and Dirk Biermann
Technische Universitt Dortmund
Germany
Abstract. One serious problem in deep-hole drilling is the formation of a dynamic
disturbance called spiralling which causes holes with several lobes. Since such lobes
are a severe impairment of the bore hole the formation of spiralling has to be prevented.
One common explanation for the occurrence of spiralling is the intersection
of time varying bending eigenfrequencies with multiples of the tool’s rotational frequency.
Little is known about which specific eigenfrequencies are crucial. Furthermore
an Underlying assumption of this explanation is, that the resulting holes in
cross-sectional view are showing as a curve with constant width. This Assumption
implicitly supposes spiralling to result from a parallel displacement of the drill head.
We in fact observed spiralling in experiments designed to force it by planning
crucial frequency intersections using a statistical-physical model proposed in earlier
work. However, not every intersection of any eigenfrequency with a multiple of the
rotational frequency led to spiralling. Furthermore we also found cases of spiralling
with two or four lobes contradict to the common assumption. After inspecting the
eigenmodes corresponding to the frequencies which caused the spiralling it turned
out that these modes commonly show a clear tilt at the drill head instead of a parallel
displacement. This tilt one the one hand in general allows to order the eigenfrequencies
by their relevance with respect to spiralling. Furthermore we now are able to
give a geometrical explanation for the spiralling development as a regenerative effect.
We use this explanation to extend our statistical-physical model by a process
model of the chips cut during the process. This model is the basis of a system for
the simulation of spiralling. Since the model contains the machine parameters it can
be used to evaluate the probability and extend of spiralling in different settings.
By this different settings can be classified into stable and instable processes and
strategies for the avoidance of spiralling can be derived. Since the statistical-physical
model includes a statistical estimation procedure for the unknown parameters these
strategies can finally be tested in real processes.
− 117 −

Statistical processes under change - Enhancing
data quality with pretests
Walter Radermacher
President of the Federal Statistical Office, Germany
walter.radermacher@destatis.de
Summary. Production of high quality statistics is the main task of Federal Statistical
Office. Technological progress, globalisation, the increasing significance and
diversification of information and its distribution, are only some general terms for
the changes we are faced with today. Needless to say, those changes strongly affect
the statistical work of the FSO and pose challenges that can only be met with innovative
and appropriate methods, to name only a few: cooperation and networks,
multiple sources, mixed-mode designs, standardisation of processes, metadata for
quality control and the use of administrative information. The point is to maximise
data quality and minimise the cost and the burden for the participants in surveys.
A prominent method for improving data quality in surveys is the use of pretests
within - or ideally before - the actual data production process. Pretests fulfill a
number of functions: they minimise non-sampling errors, they reduce the burdensomeness
for the respondents of comprehensive questionnaires and they test the
feasibility of a concept in practice. Combining quantitative and qualitative methods
for pretesting leads to significant increases in data quality. For instance, cognitive
interviewing, an accepted method mainly used in social science research, when applied
to test household surveys as well as business surveys, enables the detection of
reporting errors in surveys caused by underlying cognitive processes through which
respondents generate their answers in survey questions. Some examples from the
practice will illustrate the benefits of pretests in official statistics.
Changing conditions call for changing procedures and methods. In our work supplying
official statistics we react to the increasing demand for reliable data. Pretests
are an important example of a method which meets this need in two ways: they
improve quality control and they contribute to the userfriendliness of our surveys.
− 118 −

Automatic Dictionary Expansion Using
Non-parallel Corpora
Reinhard Rapp 1 and Michael Zock 2
1 University of Tarragona reinhard.rapp@urv.cat
2 LIF-CNRS, Marseille michael.zock@lif.univ-mrs.fr
Abstract. Automatically deriving bilingual dictionaries from manually translated
texts is an established technique that works well in practice. However, translated
texts are a scarce resource. Therefore, it is also desirable to be able to generate
dictionaries from pairs of unrelated monolingual corpora. To achieve this, we suggest
an approach that considers the crosslingual correlations between the co-occurrence
patterns of translated words. If, for example, two words X and Y co-occur more often
than expected by chance in the source language, then their translations T(X) and
T(Y) should also co-occur more frequently than expected in the target language. It
is further assumed that a small dictionary is available at the beginning, and that
the aim is to expand this base lexicon.
The approach is as follows: Using a corpus of the target language, first a cooccurrence
matrix is computed with the rows being word types from the corpus and
the columns being target words from the base lexicon. Next a word of the source
language is considered whose translation is to be determined. Using the sourcelanguage
corpus, a co-occurrence vector for this word is computed. Then, using the
dictionary, all known words in this vector are translated into the target language,
thereby discarding unknown words. The resulting vector is compared to all vectors
in the co-occurrence matrix of the target language. The vector with the highest
similarity is considered to be the translation of the source-language word.
In our experiments this method gave an accuracy in the order of 50%. To improve
the results, we perform an automatic cross-check which utilizes the dictionaries’
property of transitivity. What we mean by this is that if we have two dictionaries, one
translating from language A to language B, the other from B to C, then we can also
translate from language A to C by using the intermediate language (or interlingua)
B. That is, the property of transitivity, although having some limitations due to
word ambiguities, can be exploited to automatically generate a raw dictionary for
A to C. One might think that this is unnecessary as our corpus-based approach
also allows us to generate this dictionary directly from the respective comparable
corpora. However, having two different ways of generating the same dictionary has
the advantage that we can validate one via the other. Furthermore, by considering
several languages, additional possibilites for mutual cross-validation arise.
Key words: dictionary generation, comparable texts, translation
− 119 −

FIMIX-PLS Segmentation of Data for Path
Models with Multiple Endogenous LVs
Christian M. Ringle
University of Hamburg, Institute of Industrial Management, Von-Melle-Park 5,
20146 Hamburg, Germany, cringle@econ.uni-hamburg.de
Abstract. When applying a causal modeling approach such as partial least squares
(PLS) path modeling in empirical studies, the assumption that the data has been
collected from a single homogeneous population is often unrealistic. Unobserved
heterogeneity in the PLS estimates for the aggregate data level may result in misleading
interpretations. Finite mixture partial least squares (FIMIX-PLS; Hahn et
al., 2002) allows classifying data based on the heterogeneity of the estimates in the
inner path model. Experimental as well empirical examples (Esposito Vinzi et al.,
2007; Ringle et al., 2008) illustrate the application of FIMIX-PLS for path models
that only involve a single latent endogenous variable. This research uses a systematic
approach (Ringle, 2007) to apply the FIMIX-PLS methodology and presents
FIMIX-PLS computational experiments for a path model which includes multiple
endogenous latent variables (LVs). The results of this analysis further substantiate
the reliability of the systematic FIMIX-PLS application in more realistic situations
and provides researchers and practitioners with the certainty they require to effectively
evaluate their PLS path modeling results. If the procedure uncovers significant
heterogeneity, the analysis results in further differentiated path modeling outcomes
and, thus, allows forming more precise conclusions.
Key words: PLS Path Modeling, Heterogeneity, Finite Mixture, Segmentation
References
Esposito Vinzi, E., Ringle, C.M., Squillacciotti, S. and Trinchera, L. (2007): Capturing
and Treating Unobserved Heterogeneity by Response Based Segmentation
in PLS Path Modeling: A Comparison of Alternative Methods by Computational
Experiments. ESSEC Research Center, Working Paper No. 07019. ES-
SEC Business School Paris-Singapore.
Hahn, C., Johnson, M.D., Herrmann, A. and Huber, F. (2002): Capturing Customer
Heterogeneity using a Finite Mixture PLS Approach. Schmalenbach Business
Review, 54, 243–269.
Ringle, C.M. (2007): Segmentation for path models and unobserved heterogeneity:
The finite mixture partial least squares approach, Research Papers on Marketing
and Retailing No. 035. University of Hamburg.
− 120 −

Extreme unconditional dependence vs.
multivariate GARCH effect in the analysis of
dependence between high losses on Polish and
German stock indexes
Pawel Rokita, Krzysztof Piontek
Department of Financial Investments and Risk Management
Wroclaw University of Economics
ul. Komandorska 118/120, 53-345 Wroclaw, Poland
pawel.rokita@ae.wroc.pl, krzysztof.piontek@ae.wroc.pl
Abstract. Classical portfolio diversification methods do not take account of any
dependence between extreme returns (losses). Many researchers provide, however,
some empirical evidence that extreme-losses for various assets co-occur. If the cooccurrence
is frequent enough to be statistically significant, it may seriously influence
portfolio risk. Such effects may result from a few different properties of financial time
series, like for instance: (1) extreme dependence in an (long-term) unconditional
distribution, (2) extreme dependence in subsequent conditional distributions, (3)
time-varying conditional covariance, (4) time-varying (long-term) unconditional covariance,
(5) market contagion. Moreover, a mix of these properties may be present
in return time series. Modeling each of them requires different approaches. It seams
reasonable to investigate whether distinguishing between the properties is highly
significant for portfolio risk measurement. If it is, identifying the effect responsible
for high loss co-occurrence would be of a great importance. If it is not, the best solution
would be selecting the easiest-to-apply model. This article concentrates on two
of the aforementioned properties: extreme dependence (in a long-term unconditional
distribution) and time-varying conditional covariance.
Key words: extreme dependence, TDC, multivariate GARCH
References
Coles S., Heffernan J., Tawn J. (1999): Dependence Measures for Extreme Value
Analyses. Extremes, 2:4, 339–365.
Gouriéroux C. (1997): ARCH Models and Financial Applications. Springer.
Rokita P. (2008): Comparing extreme dependence and varying conditional covariance
concept for portfolio risk modeling (in Polish). To be published in: Taksonomia,
15.
− 121 −

Grundzüge einer generativen Korpuslinguistik
Jürgen Rolshoven
Linguistic Data Processing, Department of Linguistics, University of Cologne
rols@spinfo.uni-koeln.de
Abstract. Die Verarbeitung langer Texte ist durch die Bioinformatik stark stimuliert
worden. Dies zeigen u.a. Gusfield (1997), Böckenhauer, Bongartz (2003)
und Haubold, Wiehe (2006). Als Datenstruktur spielen Suffix Trees eine zentrale
Rolle. Für den Linguisten sind Suffix Trees jedoch nicht zur Suche von Substrings
von Bedeutung. Vielmehr ermöglichen sie die Gewinnung linguistischen
Wissens durch Aufdeckungsverfahren. Suffixe sind potentielle Morpheme in Textkorpora.
Aufdeckungsverfahren seligieren aus der Menge potentieller Morpheme
die, welche die funktions- oder bedeutungstragend sind.Dafür werden strukturalistische
Verfahren wie Austausch, Auslassung und Verschiebung eingesetzt. Formal
ergibt sich folgendes Verfahren: Suffixbäume sind äquivalent zu endlichen
Automaten und entsprechen Typ-3-Sprachen in der Chomsky-Hierarchie.Daraus
ergeben sich einfache Produktionsregeln, die mit Hilfe weiterer Information aus Suffixbäumen
in Typ-2-Regeln umgeformt werden. Diese werden wiederum in Typ-1-
Regeln transformiert. Mit diesem Vorgehen wird Sprache nicht einzeln satzweise
geparst, sondern gleichsam holistisch textbezogen. Die skizzierten Verfahren führen
zu Übergenerierungen und sind insofern generativ. Ihr deskriptives Potential reicht
über die Texte hinaus, aus denen die Regeln deriviert wurden. Dies soll
durch die Bezeichnung generative Korpuslinguistik ausdrücken. Eine generative
Korpuslinguistik verbindet durch Einsatz bioinformatischer Methoden die Stärken
des datengetriebenen korpuslinguistischen Ansatzes mit dem hypothesengetriebenen
Vorgehen generativer Grammatiken.
References
Böckenhauer, H-J., Bongartz, D. (2003): Algorithmische Grundlagen der Bioinformatik.
Teubner Verlag, Wiesbaden.
Gusfield, D. (1997): Algorithms on Strings, Trees and Sequences: Computer Science
and Computational Biology. Cambridge University Press, Cambridge, Mass.
Haubold, B., T. Wiehe (2006): Introduction to computational biology: an evolutionary
approach. Birkhäuser Verlag, Basel; Boston.
− 122 −

Cluster ensemble based on co-occurrence data
Dorota Rozmus
Department of Statistics,
Katowice University of Economics, Bogucicka 14, 40-226 Katowice
drozmus@ae.katowice.pl
Abstract. Ensemble approach have been successfully applied in the context of
supervised learning to increase the accuracy and stability of classification. Recently,
analogous techniques for cluster analysis have been suggested. Research has proved
that, by combining a collection of different clusterings, an improved solution can be
obtained.
In the traditional way of learning from data set the classifiers are built in a feature
space. However, alternative ways can be found by constructing decision rules on
similarity or dissimilarity representations, instead. In such a recognition process an
object is described by distance matrix showing the similarity to the rest of training
samples.
This research has focused on exploiting the additional information provided by
a collection of diverse clusterings to generate a co-association (similarity) matrix.
Taking the co-occurrences of pairs of patterns in the same cluster as votes for their
association, the data partitions are mapped into a co-association matrix of patterns.
This n × n matrix represents a new similarity measure between patterns. The final
data partition is obtained by clustering this matrix.
In the experiments, the behavior of partitions built on co-occurrence data is
studied.
Key words: Cluster analysis, Cluster ensemble, Co-association matrix, (Dis)similarity
representation.
References
Jain, A.K. and Fred, A. (2002): Evidence accumulation clustering based on the
k-means algorithm. Structural, Syntactic, and Statistical Pattern Recognition,
2396, 442–451.
Strehl, A. and Ghosh, J. (2002): Cluster ensembles - a knowledge reuse framework
for combining partitionings. Journal of Machine Learning Research, 3, 583–617.
Pekalska, E. and Duin, R.P.W (2000): Classifiers for dissimilarity-based pattern
recognition. In: A. Sanfeliu, J.J. Villanueva, M. Vanrell, R. Alquezar, A.K.
Jain and J. Kittler (Eds.): Proc. 15th Int. Conf. on Pattern Recognition, IEEE
Computer Society Press, Los Alamitos, 12–16.
− 123 −

Dyadic Interactions in Service Encounter -
Bayesian SEM Approach
Adam Sagan 1 and Magdalena Kowalska-Musia̷l 2
1 Chair of Market Analysis and Marketing Research Cracow University of
Economics, Rakowicka 27, 31-510 Cracow, Poland sagana@ae.krakow.pl
2 The School of Banking and Management, Armii Krajowej 4, 30-115 Cracow,
Poland m.kowalska@wszib.edu.pl
Abstract. Dyadic multirelational and sequential interactions are important aspects
in service encounters. Can be observed in B2B distribution channels, professional
services, buying centers, family decision making or WOM communications. The networks
are consisted of dyadic bonds that form dense but weak ties among actors.
The aim of paper is the identification of latent properties of dyadic interactions on
mobile phone service market. Latent variable models in relational marketing are often
concentrated either on effects of relations, or treat the relationship dimensions
as psychological constructs on individual-trait level.
We propose the approach based on bayesian latent variable modeling of social networks
with dyads as units of analysis. This approach enables to model emergent and
relational properties of actors’ interactions in dyads that are irreducible to individual
latent traits or psychological constructs.
Several competing models are developed and compared using bayesian structural
equation models of dyadic data. Bayesian SEM helps to overcome the limitations
of the more traditional solutions based on ML or WLS estimations. It is robust to
small samples that are common in social network analysis, can also be applied for
non-normal data as well as non-linear relations between latent variables.
Key words: Relationship marketing, Dyadic data, Bayesian SEM
References
Anderson, J. C. and H˚akanson, H. and Johanson, J. (1994): Dyadic Business Relationships
within a Business Network Context, Journal of Marketing, October,
1–15.
Iacobucci, D. and Hopkins, N. (1992): Modeling Dyadic Interactions and Networks
in Marketing, Journal of Marketing Research, February, 5–17.
Kenny, D.A. and Kashy, D.A and Cook, W.L. (2006): Dyadic Data Analysis. Guilford
Press, New York.
Lee, S. Y. (2007): Structural Equation Modeling. A Bayesian Approach,John Willey
and Sons, Chichester.
− 124 −

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− 125 −

Nonnegative Matrix Factorization for Binary
Data to Extract Elementary Failure Maps from
Wafer Test Images
Reinhard Schachtner 1,2 , Gerhard Pöppel 1 and Elmar Lang 2
1 Infineon Technologies AG, 93049 Regensburg, Germany
reinhard.schachtner@infineon.com
2 Institute of Biophysics, University of Regensburg, 93040 Regensburg, Germany
Abstract. We introduce a probabilistic variant of non-negative matrix factorization
(NMF) applied to binary data sets. Hence we consider binary coded images as a probabilistic
superposition of underlying continuous-valued basic patterns. An extension
of the well-known NMF procedure to binary-valued data sets is provided to solve the
related optimization problem with non-negativity constraints. We demonstrate the
performance of our method by applying it to the detection and characterization of
hidden causes for failures during wafer processing. Therefore, we decompose binary
coded (pass/fail) wafer test data into underlying elementary failure patterns and
study their influence on the quality of single wafers.
Key words: Nonnegative matrix factorization, binary data, failure patterns
References
Lee, D. and Seung, H. (1999): Learning the parts of objects by non-negative matrix
factorization, Nature, 401,788–791
− 126 −

Quality–Based Clustering of Functional Data:
Applications to Time Course Microarray Data
Theresa Scharl 1 and Friedrich Leisch 2
1 Institut für Statistik und Wahrscheinlichkeitstheorie, Technische Universität
Wien, Wiedner Hauptstr. 8-10, A-1040 Wien, Austria; Scharl@ci.tuwien.ac.at
2 Institut für Statistik, Ludwig-Maximilians-Universität München, Ludwigstraße
33, D-80539 München, Germany; Friedrich.Leisch@stat.uni-muenchen.de
Abstract. Cluster methods are typically applied to time course gene expression
data to find co–regulated genes which can finally help to reveal pathways and interactions
between genes. Clustering is either carried out on the raw data or on
functional data. In functional data analysis (e.g. Serban and Wasserman, 2005;
Tarpey, 2007) a curve is fit to each observation in order to account for time dependency.
Gene expression over time is biologically a continuous process and can
therefore be represented by a continuous function. The different curve shapes found
in a dataset can have important interpretations and characteristic patterns can be
found by clustering the estimated regression coefficients.
In this study the raw data is clustered using the well–known K–Means algorithm
as well as the quality–based cluster algorithm Stochastic QT–Clust (Scharl and
Leisch, 2006). Further, the parameters obtained by representing each gene expression
profile by a curve are clustered. Additionally mixtures of spline regression models
and mixed–effects models are applied to the data. All cluster algorithms used are
implemented in R. The different cluster methods are compared in a simulation study
on various datasets.
Key words: Cluster analysis, functional data, time course gene expression data, R
References
SCHARL, T. and LEISCH, F. (2006): The stochastic qt–clust algorithm: evaluation
of stability and variance on time–course microarray data. In Rizzi, A. and Vichi
M., editors, Compstat 2006—Proceedings in Computational Statistics, 1015–
1022, Physica Verlag, Heidelberg, Germany.
SERBAN, N. and WASSERMAN, L. (2005): CATS: Clustering after transformation
and smoothing. Journal of the American Statistical Association, 100(471), 990–
999.
TARPEY, T. (2007): Linear transformations and the k–Means clustering algorithm:
Applications to clustering curves. The American Statistician, 61(1), 34–40.
− 127 −

Multilingual knowledge based concept
recognition in textual data
Martin Schierle 1 and Daniel Trabold 2
1 Daimler AG martin.schierle@daimler.com
2 Daimler AG daniel.trabold@daimler.com
Abstract. With respect to the increasing volume of textual data which is available
through digital resources today, the identification of the main concepts in those texts
becomes more and more important and can be seen as a vital step in the analysis
of unstructured information.
Research in this area has focused on the detection of named entities like person
names or organization names, which only cover a very small part of concepts in texts.
Especially the unique mapping between concepts in different languages requires
parallel corpora which are rarely available in industrial settings.
We therefore propose a powerful new knowledge based model to recognize various
kinds of concepts even in very short and specialized texts using linguistic information
for synonym handling and word sense disambiguation.
We evaluate the proposed model on texts from the automotive domain.
References
− 128 −

Localized Logistic Regression for Discrete
Influential Factors
Julia Schiffner, Gero Szepannek, Thierry Monthé, and Claus Weihs
Faculty of Statistics, Dortmund University of Technology, 44221 Dortmund,
Germany,
schiffner@statistik.uni-dortmund.de,
weihs@statistik.uni-dortmund.de
Abstract. The two-class localized logistic regression of Tutz and Binder (2005)
is generalized to discrete explanatory variables, and applied to data from a breast
cancer study. In order to obtain a distance measure between observations of the
discrete factors a combination of the simple and the flexible matching coefficient
(Ickstadt et al., 2006) is taken. Applying the method of Tutz and Binder (2005)
with this distance measure to the example data, localized models lead to smaller
misclassification rates than the corresponding global ones. Moreover, the best classification
rule found gives one of the smallest misclassification rates ever obtained
for the example data. The results of Monthé (2008) are extended by an automatic
variable selection.
Key words: Localized logistic regression, Matching coefficients, SNP data
References
Ickstadt, K., Mueller, T., and Schwender, H. (2006): Analyzing SNPs: Are There
Needles in the Haystack? CHANCE, 19(3), 22–27.
Monthé, Th. (2008): Lokalisierte Logistische Regression bei diskreten Variablen. Master
thesis, Faculty of Statistics, Dortmund University of Technology.
Tutz, G. and Binder, H. (2005): Localized Classification. Statistics and Computing,
15, 155–166.
− 129 −

Localized Classification Using Mixture Models
Julia Schiffner and Claus Weihs
Faculty of Statistics, Dortmund University of Technology, 44221 Dortmund,
Germany,
schiffner@statistik.uni-dortmund.de
Abstract. In the literature a variety of classification methods can be found that can
be called ‘local’ because they concentrate – in different senses – on one or multiple
small regions of the data space. One type of local methods that may be beneficial
in case of heterogeneous classes is based on mixture models. It is assumed that
data are generated by a finite number of sources and that each source can produce
data of one or multiple classes. Models valid for single sources can be referred to
as ‘local models’ that can be aggregated to a global mixture model. Mixture based
classification methods have been described by several authors (see references), but
the relationships and differences between the underlying models are not clear. A
consistent description of these models and the resulting Bayes classification rules is
presented. Moreover, it is shown how Bayes rules can be derived if in distinct local
models different variable subsets separate the classes. Finally, several methods for
class posterior estimation are described and an application to sound data is shown,
where the register of different instruments is predicted by timbre.
Key words: Local classification methods, Mixture models, Bayes rules
References
Hastie, T. J. and Tibshirani, R. J. (1996): Discriminant Analysis by Gaussian Mixtures.
Journal of the Royal Statistical Society B, 58(1), 155–176.
Szepannek, G. and Weihs, C. (2006): Local Modelling in Classification on Different
Feature Subspaces. In: P. Perner (Ed.): Advances in Data Mining. Springer,
Berlin, 226-238.
Titsias, M. K. and Likas, A. C. (2001): Shared Kernel Models for Class Conditional
Density Estimation. IEEE Transactions on Neural Networks, 12(5), 987–997.
Titsias, M. K. and Likas, A. C. (2002): Mixture of Experts Classification Using a
Hierarchical Mixture Model. Neural Computation, 14, 2221–2244.
Weihs, C., Szepannek, G., Ligges, U., Luebke, K., and Raabe, N. (2006): Local Models
in Register Classification by Timbre. In: V. Batagelj, H.-H. Bock, A. Ferligoj,
and A. Ziberna (Eds.): Data Science and Classification. Springer, Berlin, 315-
322.
− 130 −

Comparison of four estimators of the
heterogeneity variance for meta-analysis
Peter Schlattmann
Dept. of Biostatistics and Clinical Epidemiology
Charité Universitätsmedizin Charitéplatz 1, 10117 Berlin
peter.schlattmann@charite.de
Summary. The analysis of heterogeneity is a crucial part of each meta-analysis.
In order to analyze heterogeneity often a random effects model which incorporates
variation between studies is considered. It is assumed that each study has its own
(true) exposure or therapy effect and that there is a random distribution of these true
exposure effects around a central effect. The variability between studies is quantified
by the heterogeneity variance.
In order to compare the performance of four estimators of the heterogeneity variance
a simulation study was performed. This study compared the Dersimonian-Laird
(1986) estimator with the maximum-likelihood estimator based on the normal distribution
for the random effects. Further comparators were the simple heterogeneity
(SH) variance estimator proposed by Sidek and Jonkman (2005).
All of the afore mentioned methods assume a normal distribution for the random
effects. This assumption may be true or not. Thus an alternative estimator of
the heterogeneity variance is based on a finite mixture model (Böhning, Dietz,and
Schlattmann, 1998).
This simulation study investigates these four estimators, when sampling from
discrete distributions, i.e. the major assumption of a normal distribution for the
random effects is not fulfilled. In this setting the simulation study investigates bias,
standard deviation and mean square error (MSE) of all four estimators.
Key words: Meta-Analysis, Heterogeneity, Simulation, Finite mixture model
References
Böhing, D., Dietz, E. and Schlattmann, P. (1998): Recent developments in computer
assisted mixture analysis. Biometrics, 54, 283-303
DerSimonian, R. and Laird, N. (1986): Meta-analysis in clinical trials. Controlled
Clinical Trials,7,177-188
Sidek, K. and Jonkman, J. (2005): Simple heterogeneity variance for metaanalysis.
JRSS Series C, 54, 367-384
− 131 −

Machine learning applications of positive
definite kernels
Prof. Dr. Bernhard Schölkopf
MPI for Biological Cybernetics
Spemannstrasse 38
72076 Tübingen
bernhard.schoelkopf@tuebingen.mpg.de
Summary. Support vector machines and other kernel methods have become one of
the most widely used techniques in the field of machine learning. I will present my
thoughts on what made them popular and what may (or may not) keep them going.
I will also discuss applications in different domains, including computer graphics.
− 132 −

Age Distributions for costs in drug prescription by
practitioners and for DRG-based hospital treatment
Reinhard Schuster, Eva v. Arnstedt
Medical Review Board of the Statutory Health Insurance in North Germany,
23554 Lübeck, Germany, Reinhard.Schuster@mdk-nord.de
Abstract. Purpose: We analyse age-dependent fractions of patients with costs above a treshold
value in dependency of that value both in drug application outside hospitals as well as
in DRG-based hospital treatment. We compare the results of different German regions and
different statutory insurances. The outcome of age-dependency of costs is highly important in
respect to demographic changes. Design/Methodology/Approach/Algorithm: We use drug
prescription data of practitioners and data of DRG-based hospital treatment from several statutory
insurances and several regions. We use a nonparametric functional equation with a geometric
background which generates an one parametric family of logconcave distributions including
the normal distribution. The mentioned functional equation is also related to Verhulst
growth. Results: The data can be fitted by log-concave distributions and we get numerically
stable computations. The respective logarithms are concave with respect to both variables age
and costs. We find that independent of the absolute threshold value there is always a decrease
in the fraction of high-cost patients above a certain age. So we do not find a monotone increase
of costs with age. Research Limitations/Implications: The statistically reported data
basis for age-dependent costs is poor in general with respect to specific details, especially
if a (pseudonymized) patient-identifier is necessary. Practical Implications: Demographic
changes are important for a large range of induced implications. Often it is implicated that
the costs are strictly increasing with age. If this turns out not to be true in general, costs
are depending much more sensible on the exact (demographically changing) age distribution
of the population, which should be analysed in that direction. Originality/Value: The agedependent
resolution of officially stated statistic reports is poor in general. We state a stable
non-parametric model with high resolution.
Key words: Drug Application, Age Distribution, DRG-System, Statutory Health Insurance
References
SCHUSTER, R.: Komponentenzerlegungen, Strukturen und Invarianten zu GKV-
Arzneimittelverordnungsdaten. Journal of Public Health 4 (2003), 293-305.
− 133 −

The Late Neolithic flint axe production on the
Lousberg (Aachen, Germany) — An
extrapolation of supply and demand and
population density
Daniel Schyle
Institut für Ur- und Frühgeschichte Universitt zu Köln
daniel.schyle@uni-koeln.de
Abstract. The tabular flint seams within the cretaceous limestone slab once covering
the Lousberg in Aachen (Germany) were completely exploited by systematic
opencast mining during the time between approximately 3800 and 3000 years
CalBC. The Lousberg-flint, easily identifiable by its tabular shape and its characteristic
colours, was processed on-site almost exclusively for the production of
axe-roughouts, which were distributed over distances up to 280 km mainly to Westphalia,
but also to Hessen, Rheinland-Pfalz and into Belgium and the Netherlands.
An excavation at the Lousberg was carried out under the direction of J. Weiner
between 1978 and 1981. This contribution presents an extrapolation of the total
amount of axe-roughouts produced at the site, based on the results of refittings and
the counts of random samples of the knapping waste excavated from the mining
dumps. The corresponding demand for axes per household and generation is estimated
from axe distributions and frequencies in several well dated and preserved
lakeshore dwellings of Southern Germany and Switzerland. To estimate the population
density within the distribution area of Lousberg-axes, which is almost devoid of
Late Neolithic settlement traces other than only roughly dated surface assemblages,
the approximate size of the core-distribution area is determined by the site density
mapping method (”Isolinien-Fundstellendichtekartierung”) recently developed by A.
Zimmermann and collaborators of the Institut fr Ur- und Frhgeschichte at the University
of Cologne. The contribution will focus on the problems in comparing the
results based on the distribution of Lousberg-axes to the results recently obtained
on settlement distributions of the Linearbandkeramik (LBK) in the Rhineland. The
research is part of a project aimed at the final publication of the Lousberg finds,
which was funded by the Deutsche Forschungsgemeinschaft (DFG).
Key words: Abstract, Layout, Submission guideline
− 134 −

Time Related Features for Alarm Classification
in Intensive Care Monitoring
Wiebke Sieben
Department of Statistics, Technische Universitt Dortmund, 44227 Dortmund,
Germany sieben@statistik.tu-dortmund.de
Abstract. Traditional patient monitoring systems in intensive care are based on
simple threshold alarms. These systems compare the measurement of a vital sign
with a threshold set by the clinical staff and trigger an alarm when the threshold
is crossed. Although there are some more sophisticated rules already incorporated
in modern monitoring devices the false alarm rate has remained very high (Tsien
Fackler 1997, Chambrin 2001). Machine learning techniques, and particularly decision
trees have proven suitable for alarm classification. As the misclassification rate
of non life-threatening situations is to be minimized under the constraint that the
misclassification rate of life-threatening situations is close to zero, standard techniques
need to be improved. Modified Random Forests (Sieben, Gather 2007) have
been shown to do this successfully. So far only the measurements of the point in
time when an alarm was triggered were used for classification. As physicians always
take the character of changes over time in a patients’ health status into account for
a diagnosis there might exist valuable information to be extracted from the time
series. We study the use of time related features in combination with the modified
Random Forest approach in terms of improvements in the classification results.
References
CHAMBRIN, M.-C. (2001): Alarms in the Intensive Care Unit: How Can the Number
of False Alarms Be Reduced?. Critical Care, 5 (4), 184–188.
SIEBEN, W., GATHER, U. (2007):Classifiing Alarms in Intensive care - Analogy
to Hypothesis Testing. in LNCS Series: Proceedings of the 11th Conference on
Artificial Intelligence in Medicine, Vol.4594/2007, eds. R. Bellazzi, A. Abu-
Hanna, J. Hunter, Springer, Berlin/Heidelberg, 130–138.
TSIEN, C.L., FACKLER, C. (1997): Poor Prognosis for Existing Monitors in the
Intensive Care Unit. Critical Care Medicine, 25 (4), 614–619.
Keywords
CLASSIFICATION, INTENSIVE CARE MONITORING, FALSE ALARMS
− 135 −

’CMA’ - Steps in developing a comprehensive
R-toolbox for classification with microarray
data and other high-dimensional problems
Martin Slawski, Anne-Laure Boulesteix, and Martin Daumer
Sylvia Lawry Centre for MS Research, Hohenlindenerstr. 1, D-81677 München
Martin.Slawski@campus.lmu.de, boulesteix@slcmsr.org, daumer@slcmsr.org
Abstract. Microarray studies have stimulated the development of new approaches
and motivated the adaptation of known traditional methods for class prediction with
high-dimensional data. There already exist numerous sofware packages implementing
single methods for microarray-based classification and in addition two synthesis
packages: MLInterfaces by V. Carey and R. Gentleman (2007) and MCRestimate
by Ruschhaupt et al (Stat Appl Genet Mol Biol 2004, 3:37 ), available from the
www.bioconductor.org platform. Conceptually, the R package CMA is more related
to the second one, focussing on comparative model evaluation according to accepted
’good pratice’ standards/guidelines (Dupuy and Simon, J Natl Cancer Inst 2007,
99:147-157 ), an aspect neglected by MLInterfaces, though still widely used. In
a nutshell, CMA provides a uniform interface to a total of more than 20 supervised
classification methods, comprising classical approaches such as discriminant analysis
or penalized multinomial logistic regression, dimension reduction by Partial Least
Squares, and more sophisticated methods, e.g. Support Vector Machines, Neural
Networks or boosting techniques.
The evaluation of the constructed classifiers is based on repeated splittings into
learning and test sets or related approaches (e.g. bootstrap). For each learning set
separately, variable selection can be performed optionally, either by a collection of
simple tests or by advanced techniques such as the lasso, elastic net or componentwise
boosting. In the last step, hyperparameter optimization and model evaluation
are carried out via a ’nested’ cross-validation procedure. The outer loop is used for
classifier evaluation while appropriate values for the hyperparameters are determined
in the inner loop.
CMA is implemented entirely in S4 classes (J. Chambers, Programming with data,
1998). Its modular construction makes the incorporation of new methods easy. Furthermore,
it is intended to be user-friendly by providing a multitude of pre-defined
methods for summarizing and visualizing classifier evaluation and comparison.
A preliminary version of CMA is planned to be available in the next Bioconductor
release in April 2008.
Keywords
High-dimensional data, classification, validation, statistical software
− 136 −

Generating Collective Intelligence
Vassilios Solachidis 1 , Phivos Mylonas 2 , Andreas Geyer-Schulz 3 , Bettina
Hoser 3 , Sam Chapman 4 , Fabio Ciravegna 4 , Stefen Staab 5 ,Costis
Contopoulos 6 , Ioanna Gkika 6 ,PavelSmrz 7 , Yiannis Kompatsiaris 1 ,and
Yannis Avrithis 2
1
Centre of Research and Technology Hellas, Informatics and Telematics Institute,
Km Thermi-Panorama Road, Thermi-Thessaloniki, GR 570 01, Greece {vsol,
ikom}@iti.gr
2
National Technical University of Athens, Image, Video and Multimedia Systems
Laboratory, Iroon Polytechneiou 9, Zographou Campus, Athens, GR 157 80,
Greece {fmylonas, iavr}@image.ntua.gr
3
Department of Economics and Business Engineering, Information Service and
Electronic Markets, Kaiserstraße 12, Karlsruhe 76128, Germany,
{andreas.geyer-schulz, bettina.hoser}@kit.edu
4
University of Sheffield, Department of Computer Science, Regent Court, 211
Portobello Street, S1 4DP, Sheffield, UK, {s.chapman, fabio}@dcs.shef.ac.uk
5
Universität Koblenz-Landau, Information Systems and Semantic Web,
Universitätsstraße 1, 57070 Koblenz, Germany, staab@uni-koblenz.de
6
Vodafone-Panafon (Greece), Technology Strategic Planning - R&D Dept.,
Tzavella 1-3, Halandri, 152 31, Greece {Costis.Kontopoulos,
Ioanna.Gkika}@vodafone.com
7
Brno University of Technology, Faculty of Information Technology, Bozetechova
2, CZ-61266 Brno, Czech Republic, smrz@fit.vutbr.cz
Abstract. In this paper we provide a foundation for a new generation of services
and tools. We define new ways of capturing, sharing and reusing information and
intelligence provided by single users and communities, as well as organizations by
enabling the extraction, generation, interpretation and management of Collective
Intelligence from user generated digital multimedia content. Different layers of intelligence
will be generated, which together constitute the notion of Collective Intelligence.
The latter emerges from the collaboration and competition among many
individuals and forms an intelligence that seemingly has a mind of its own. The
automatic generation of Collective Intelligence constitutes a departure from traditional
methods for information sharing, since information from both the multimedia
content and social aspects will be merged, while at the same time the social dynamics
will be taken into account. In the context of this work, we shall present two case
studies. Initially, an Emergency Response case study will be tackled, where users
provide intelligence about large scale emergencies, empowering a more effective and
informed emergency action and at the same time receive information on how to act.
A Consumers Social Group case study will follow, providing enhanced publishing
tools to support group activities (e.g. organization of team events) and the ability
to extract meta-information from content sources and group discussions. Both Use
Cases denote the important effect of Collective Intelligence as well as its leverage
for private, commercial and public purposes.
− 137 −

Analysis of polyphonic musical time series
Katrin Sommer and Claus Weihs
Lehrstuhl für Computergestützte Statistik
Technische Universität Dortmund, D-44221 Dortmund
sommer@statistik.tu-dortmund.de
Abstract. A general model for pitch tracking of polyphonic musical time series will
be introduced. Based on a model of Davy and Godsill (2002) the different pitches
of the musical sound are estimated with MCMC methods simultaneously. Additionally
a preprocessing step is designed to improve the estimation of the fundamental
frequencies (Sommer and Weihs (2008)). The preprocessing step compares real audio
data with an alphabet constructed from the McGill Master Samples (Opolko
and Wapnick (1987)) and consists of tones of different instruments. The tones with
minimal Itakura-Saito distortion (Gray et al. (1980)) are chosen as first estimates
and as starting points for the MCMC algorithms. Furthermore the implementation
of the alphabet is an approach for the recognition of the instruments generating the
musical time series. Results are presented for mixed monophonic data from McGill
and for self recorded polyphonic audio data.
Key words: MCMC, musical time series, polyphony, alphabet
References
Davy, M. and Godsill, S. J. (2002): Bayesian Harmonic Models for Musical Pitch
Estimation and Analysis. Technical Report 431. Cambridge University Engineering
Department.
Gray, R., Buzo, A., Gray, A. and Matsuyama, Y. (1980): Distortion Measures for
Speech Processing. IEEE Transactions on Acoustics, Speech, and Signal Processing
ASSP-28, 367–376.
Opolko, F. and Wapnick, J. (1987): McGill University Master Samples [Compact
disc]: Montreal, Quebec: McGill University.
Sommer K. and Weihs C. (2006): Using MCMC as a stochastic optimization procedure
for music time series. In Batagelj V, Bock HH, Ferligoj A, Ziberna A
(Eds.): Data Science and Classification, Springer, Heidelberg, 307–314.
Sommer, K. and Weihs, C. (2008): A comparative Study on polyphonic musical
time series using MCMC methods. In C. Preisach, H. Burkhardt, L. Schmidt-
Thieme and R. Decker (Eds.): Data Analysis, Machine Learning, and Applications.
Springer, Berlin.
− 138 −

Trust as a Key Determinant of Loyalty and its
Moderators
Angela Sommerfeld 1
Institut für Marketing, Humboldt-Universität zu Berlin angelaso@umich.edu
Abstract. Theorizing that successful relational exchanges are motivated by trust
and commitment, theory implicitly assumes that transactional and weak relational
exchanges are not similarly motivated. As such Garbarino & Johnson (1999) showed
that trust is a peripheral evaluation, not predictive for purchase intentions in weak
(individual ticket buyers) but for strong relationships (subscribers of the theatre).
Extending their work we take a more theory-based approach to develop and test
moderating hypotheses of the trust ? purchase intention relation beyond their variable
type of contractual relationship. Based on a survey of 575 business-to-business
customers we test the following proposed moderators: two facets of perceived risk
notably performance risk and consequentiality, perceived switching cost, and length
of the relationship between companies. Different methods have been employed to test
the moderations (Multiple-Groups, Kenny-Judd-Models, and Quasi-ML). Especially
the Quasi-ML method, which we apply for a simultaneous test of several moderation
hypotheses, represents a statistically efficient estimation method for SEMs with
multiple latent interaction effects (Klein & Muthen 2007). Depending on the method
and their varying properties several hypotheses could be confirmed. The paper seeks
to make three key contributions. First it gives a theory-based account of boundary
conditions for the relevance of trust in exchange relationships between companies.
Since there have been conflicting opinions on the role of risk in exchange between
companies, a second contribution of the paper is to clarify this role by thoroughly
testing both moderating and mediating hypotheses. Testing interactions in a structural
equation framework is not a straightforward task. Thus a third contribution is
to illustrate strengths and weaknesses of different methods for a substantive research
question with a real world data set.
Key words: Trust, Risk, Switching Cost, Multiple
References
Klein, A.G. and Muthen, B.O. (2007): Quasi Maximum Likelihood Estimation of
Structural Equation Models With Multiple Interaction and Quadratic Effects.
Multivariate Behavioral Research, 42, 647–673.
− 139 −

Generating Fictitious Training Data for Credit
Client Classification
Klaus B. Schebesch 1 and Ralf Stecking 2
1
Faculty of Economics, University ”Vasile Goldis”, Arad, Romania
kbsbase@gmx.de
2
Faculty of Economics, University of Oldenburg, D-26111 Oldenburg
ralf.w.stecking@uni-oldenburg.de
Abstract. In recent work we started investigating the effects of using fictitious
training examples in addition to the empirical training examples for a credit scoring
problem. Fictitious training points added by a very simple procedure lead to
some interesting effects in the context of SVM (support vector machine) classifier
modeling. For instance, the resulting out-of-sample performance measures of such
preliminary models are not entirely obvious. However, by using SVM, we also can
observe the change in support vector formation subject to fictitious training points.
Such information may prove instrumental in producing fictitious training points
which are (more) problem dependent. We also explore connections to generative,
similarity based and template based learning which, in a related context receive
some attention in recent classification literature. We then report on the results of
using different types of fictitious training examples in SVM credit client classification.
Finally, in order to generalize these results, evaluation of SVM with different
kernel functions using various fictitious training data sets is presented.
Key words: Fictitious training data, Data similarity, Support vector machine,
Credit scoring
References
DUIN, R.P.W. and PEKALSKA, E. (2007): The Science of Pattern Recognition.
Achievements and Perspectives. In: W. Duch, J. Mandziuk (eds.), Challenges
for Computational Intelligence, Studies in Computational Intelligence, Springer
HOCHREITER, S. and OBERMAYER, K. (2006). Support vector machines for
dyadic data. Neural Computation, 18, 1472-1510
LAUB, J., ROTH, V., BUHMANN, J.M. and MÜLLER, K. (2006): On the information
and representation of non-Euclidean pairwise data. Pattern Recognition,
39, pp. 1815-18266
STECKING, R. and SCHEBESCH, K.B. (2007): Improving Classifier Performance
by Using Fictitious Training Data? A Case Study. Accepted for publication in
Operations Research Proceedings 2007.
− 140 −

Clustering Association Rules with
Fuzzy Concepts
Matthias Steinbrecher 1 and Rudolf Kruse 1
Department of Knowledge Processing and Language Engineering
Otto-von-Guericke University of Magdeburg
Universitätsplatz 2, 39106 Magdeburg, Germany
{msteinbr,kruse}@iws.cs.uni-magdeburg.de
Abstract. Association rules constitute a widely accepted technique to identify frequent
patterns inside huge volumes of data. Practioneers prefer the straightforward
interpretability of rules, however, depending on the nature of the underlying data
the number of induced rules can be intractable large. Even reasonably sized result
sets may contain a large amount of rules that are uninteresting to the user because
they are too general, are already known or do not match other user-related intuitive
criteria. We allow the user to model his conception of interestingness by means of linguistic
expressions on rule evaluation measures and compound propositions of higher
order (i. e., temporal or spatial changes of rule properties). Multiple such linguistic
concepts can be considered a set of fuzzy patterns [?] and allow for the partition of
the initial rule set into fuzzy fragments that contain rules of similar membership to a
user’s concept [?,?,?]. With appropriate visualization methods that extent previous
rule set visualizations [?] we allow the user to instantly assess the matching of his
concepts against the rule set.
Key words: Association Rules, Fuzzy Clustering, Exploratory Data Analysis
References
1.Dubois, D., Prade, H., Testemale, C.: Weighted Fuzzy Pattern Matching. Fuzzy
Sets and Systems 28(3) (1988) 313–331
2.Höppner, F., Klawonn, F., Kruse, R., Runkler, T.: Fuzzy Clustering. Wiley,
Chichester, United Kingdom (1999)
3.Döring, C., Lesot, M.J., Kruse, R.: Data Analysis with Fuzzy Clustering Methods.
Computational Statistics & Data Analysis 51(1) (2006) 192–214
4.Kruse, R., Döring, C., Lesot, M.J.: Fundamentals of fuzzy clustering. In
de Oliveira, J.V., Pedrycz, W., eds.: Advances in Fuzzy Clustering and its Applications.
John Wiley & Sons (2007) 3–30
5.Steinbrecher, M., Kruse, R.: Visualization of Possibilistic Potentials. In: Foundations
of Fuzzy Logic and Soft Computing. Volume 4529 of Lecture Notes in
Computer Science., Springer Berlin / Heidelberg (2007) 295–303
− 141 −

Who’s Afraid of Statistics? – Measurement and
Predictors of Statistics Anxiety in German
University Students
Carolin Strobl 1 and Friedrich Leisch 2
1 Institut für Statistik, Ludwig-Maximilians-Universität München
carolin.strobl@stat.uni-muenchen.de
2 friedrich.leisch@stat.uni-muenchen.de
Abstract. The measurement of statistics anxiety and the relationship between
statistics anxiety and several socio-demographic and educational factors was investigated
in a survey on over 600 German university students. The attitude towards
statistics was measured by means of the Affect and Cognitive Competence scales of
the Survey of Attitudes Towards Statistics (SATS, Schau et al., 1995). Additional
items covered, amongst others, prior mathematics experience and achievement, time
and activity since high school graduation as well as items on the students’ strategy
applied in mathematics courses, which was not considered in earlier studies. An
anxiety indicator was derived from the SATS scales by means of cluster analysis
in order to separate a group of students with high levels of statistics anxiety from
those with moderate and low levels of anxiety. Using this anxiety indicator as the
response, a set of relevant predictor variables was identified by means of random forest
variable importance scores and further explored in a logistic regression model.
Our results show that the SATS Affect and Cognitive Competence scales are well
suited for identifying students with high levels of negative attitude against statistics,
even though potential effects of the translation into German were noticeable
for the positively worded items. Predictors found relevant for statistics anxiety were
gender, mathematics taken as an intensive course in high school, prior (perceived)
mathematics achievement, prior mathematics experience as well as two of the newly
included items on students’ strategy applied in mathematics courses in high school:
Students who named practicing as their strategy were less likely, while students who
named memorizing as their strategy were more likely to show statistics anxiety.
Key words: Attitude towards statistics, SATS, Statistics education
References
Schau, C., Stevens, J., Dauphinee, T. L. and Vecchio, A. D. (1995): The development
and validation of the survey of attitudes toward statistics. Educational and
Psychological Measurement, 55 (5), 868–875.
− 142 −

A New, Conditional Variable Importance
Measure for Random Forests
Carolin Strobl 1 and Achim Zeileis 2
1 Department of Statistics, Ludwig-Maximilians-Universität München
carolin.strobl@stat.uni-muenchen.de
2 Department of Statistics and Mathematics, Wirtschaftsuniversität Wien
Achim.Zeileis@wu-wien.ac.at
Abstract. Random forests are becoming increasingly popular in many scientific
fields for assessing the importance of predictor variables (cf., e.g., Lunetta et al.,
2004) because they can cope with “small n large p” problems, complex interactions
and even with highly correlated predictor variables. Their variable importance can
help identify relevant predictors even if they are highly correlated, while in classical
regression models often only one representative of a group of correlated predictors is
included. However, currently-used variable importance measures can be biased, e.g.,
towards variables with many categories (Strobl et al., 2007) or correlated predictor
variables (Archer and Kimes, 2008). While the former issue can be addressed by
changing the resampling scheme in the tree growing process (Strobl et al., 2007),
the latter is due to the permutation scheme employed in the computation of the
variable importance. Here we suggest a new, conditional permutation scheme that
is more suited to measure the degree of association of each predictor variable with
the response. The resulting conditional variable importance can be used to rank the
predictor variables more reliably.
Key words: Feature selection, Correlation, Variable importance, Permutation tests
References
Archer, K. and Kimes, R. (2008): Empirical characterization of random forest variable
importance measures. Computational Statistics & Data Analysis, 52(4),
2249–2260.
Lunetta, K.L., Hayward, L.B., Segal, J., Eerdewegh, P.V. (2004): Screening largescale
association study data: Exploiting interactions using random forests. BMC
Genetics, 5:32.
Strobl, C., Boulesteix, A.-L., Zeileis, A. and Hothorn, T. (2007): Bias in random
forest variable importance measures: Illustrations, sources and a solution. BMC
Bioinformatics, 8:25.
− 143 −

Conjoint Analysis within the field of customer
satisfaction problems a model of composite
product/service
Piotr Tarka
School of Banking in Poznan
Department of Organization and Management
Poland
piotr.tarka@wsb.poznan.pl
Abstract. This paper describes how the benefits of conjoint analysis can be
adapted to measuring performance criteria in the customer service area. The paper
points out how a single composite model can be built, incorporating a wide
range of customer key choice criteria, including service. Author makes a mark to a
specific problems. One of them, is the apparent distortion in computed utility values
that arises in circumstances where global macro variables are traded-off against more
micro topics. This can lead to dramatic underestimation of the overall contribution
or importance of macro issues. To address this concern, author discuss an approach
known as dual scaling for eliminating the bias. Another drawback to the approach
in customer service studies is the limited number of variables that can be addressed
by a typical conjoint study. This makes it difficult to cover the large range of service
topics typically examined in a customer satisfaction study. The paper argues
that, this limits the scope of both classical conjoint studies and current customer
satisfaction approaches.
Key words: Conjoint Analysis, Customer satisfaction problems
− 144 −

Optimal VDSL Expansion taking into
Consideration of Infrastructure Restrictions
and Marketing Requirements
Klaus Thiel 1
T-Online, T-Online-Allee 1, 64295 Darmstadt k.thiel@t-online.net
Abstract. The expansion of the Very High Speed Subscirber Line (VDSL) network
in Germany is a billion-weighted prestigious infrastructure project. VDSL enables
a transfer rate of 50 megabytes per second. With it, for example, so-called entertainment
customer can receive two movies byte-parallel in High Definition Television
(HDTV) and internet surfing and telephoning is also possible. Within the
b2b-sector many new applications like telecommuting in virtual teams around the
world, telemedicine have become feasible. Currently Deutsche Telekom has developed
VDSL in 27 cities and for 2008 the VDSL expansion is planned for further 23
cities. The optimal choice of the VDSL expansion areas primarily depends on infrastructure
restrictions as well as on marketing requirements. In order to execute a
spatial optimisation procedure, all the important infrastructure and marketing information
must be converted to vector data by digitizing und subsequently importing
into a Geo-Information-System (GIS).
The most important GIS provider in Germany are Microm with MicromGEO
and ESRI with ArcGIS. In order to select the most suitable system, regarding the
mentioned problem, both systems have to be evaluated on the basis of objective
test criteria. Test criteria are the quality of geo-referencing of address-data and
the mapping-quality of different spatial levels. In order to choose the most suitable
spatial level (e.g. city, post-code, dialling-code, municipality), several analysis have
been executed. In the next step, a spatial scoring has been developed and imported
into the GIS in order to ensure, that those areas with the highest VDSL customer
equity potential will be the first to be expanded. Finally, using the spatial scoring,
a spatial potential-ranking has been calculated on the basis of which the optimal
VDSL expansion can be planned and executed.
Key words: Customer Equity, Geo-Information-System, Optimal VDSL Expansion
− 145 −

Evaluate the data structure and identify
homogenous spatial units in the data base
”Sustainability issues in sensitive areas” of the
EU-FP6 Integrated Project SENSOR
Nguyen Xuan Thinh 1 , Leander Küttner 1 , and Gotthard Meinel 1
Leibniz Institute of Ecological and Regional Development (IOER), Weberplatz 1,
01217 Dresden, Germany, ng.thinh@ioer.de, l.kuettner@ioer.de,
g.meinel@ioer.de
Abstract. SENSOR (Sustainability Impact Assessment: Tools for Environmental,
Social and Economic Effects of Multifunctional Land Use in European Regions) is
an Integrated Project within the 6th Framework Research Programme of the European
Commission (33 research partners from 15 countries). The SENSOR Project
is structured into seven interrelated modules M1-M7. For the Module M6 ”Sustainability
issues in sensitive areas”, a data base with more than 800 000 entries has
been established. Whereby Lusatia, Silesia, Eisenwurzen, High Tatra, Valais, Estonia
coastal zone, and Malta were selected as sensitive area case studies (SACS).
Using ACCESS, SPSS and ArcMap we conduct a comparative analysis and evaluate
this M6 database with the view to the theoretical sustainability indicators defined
in the ModuleM2. We then determine similarities and dissimilarities between data
from different SACS. By applying adequate cluster analysis we identify homogenous
spatial units of selected SACS in order to find out generalisable and specific sustainability
characteristics in the seven case studies. As example we describe the case
study of Lusatia more in detail. The area of Lusatia is divided in several local area
units (LAU2), which are qualified for a statistical examination by a high number of
entries and available variables. As base we choose a set of 25 variables related to
sustainable land use issues. Using a factorial analysis we determine the significant
variables and use them as representatives to characterise typical land use clusters. In
a next step the clusters were identified by a combination of hierarchical and k-mean
cluster analysis methods. To describe the situation at different time points and the
development in the period between them, we repeat the procedure of cross sectional
analysis of 1996 for 2004. The results of the statistical analysis are presented in
ArcMap visualisations. Although the procedure and the variable base are the same,
the results differ and reveal so the relevant land use trends within the general social
transformation process of the 1990ies.
Key words: EU Integrated Project SENSOR, Comparative Analysis, Similarity,
Disimilarity, SENSOR M6 Indicators, Cluster Analysis
− 146 −

Mining ideas from textual information
Dirk Thorleuchter
Fraunhofer Institut für Naturwissenschaftlich-Technische Trendanalysen,
D-53879 Euskirchen, Appelsgarten 2, Germany
dirk.thorleuchter@int.fraunhofer.de
Abstract. This paper describes an approach to find automatically new technological
ideas in textual information. On the basis of (Thorleuchter (2008)) the existing
theoretical algorithm is enlarged in consideration of text mining approaches
like stemming, term frequency etc. (Ferber (2003)) and ”creativity technique” approaches
from literature (Dean et al. (2001)). The aim of the new algorithm is to
find ideas by using a general stop word list, because up to now the existing approach
is based on the inefficient usage of a (domain) specific stop word list specific
created for the analyzed text.
This new approach is evaluated with non-proprietary data and it is realized as webbased
application, named ”Technological Idea Miner” that can be used for further
testing and evaluation. The presentation of the identified ideas will be displayed in
consideration of cognitive research knowledge like described in (Puppe et al. (2003)).
Key words: Textmining, Knowledge, Discovery, Ideas
References
Dean, G.,Hender, J.M., Nunmaker, J.F. and Rodgers, T.L. (2001): Improving Group
Creativity. In: Sprague, R., (Hrsg.): Proceedings of the 34th Hawaii International
Conference on System Sciences - 2001. IEEE Publishing, Maui (USA),
1070.
Ferber, R. (2003): Information Retrieval. dpunkt.verlag, Heidelberg, 41.
Puppe, F., Stoyan, H. and Studer, R. (2003): Knowledge Enineering. In: G. Görz, C.-
R. Rollinger and J. Schneeberger (Eds.): Handbuch der Künstlichen Intelligenz.
4. Auflage, Oldenbourg, München, 612.
Thorleuchter, D. (2007): Finding new technological ideas and inventions with text
mining and technique philosophy. In: C. Preisach, H. Burkhardt, L. Schmidt-
Thieme, R. Decker (Eds.): Data Analysis, Machine Learning, and Applications.
Springer, Heidelberg-Berlin.
− 147 −

Mining technologies in security and defense
Dirk Thorleuchter
Fraunhofer Institut für Naturwissenschaftlich-Technische Trendanalysen,
D-53879 Euskirchen, Appelsgarten 2, Germany
dirk.thorleuchter@int.fraunhofer.de
Abstract. In the last years, the rising asymmetrical threat is causing governments
to pay more attention to security, especially in technological areas. New and ever
more complex tasks in areas concerned with defense against these new types of threat
require additional research and development of new techniques. For this reason,
national and European governments are increasingly funding security and defense
(S&D) based technological research.
In this paper, we give an overview about the technological landscape of S&D
by presenting different S&D-technologies and their relationships like described in
(Geschka et al. (2005)) and (Reiß (2006)). Therefore we firstly identify technologies
from different technological S&D-taxonomies and we secondly identify innovative
S&D-research projects. The research projects are classified according to technologies
and on that basis the relationships between technologies are presented.
In detail, text documents are represented as vectors in vector space model using
term frequency and corpus-based term co-occurrence data. We use Jaccard’s coefficient
(Ferber(2003)) to measure similarity and we use fuzzy alpha-cut method for
classification. Structured documents (XML) are used as data source and drain.
To realize this approach, we present a web application ”S&D Technology Miner”
for planning support to research program planners and to researchers, which acquire
funding in this area but also for testing and evaluating the approach.
Key words: Security, Defense, Technology, Textmining, Classification
References
Ferber, R. (2003): Information Retrieval. dpunkt.verlag, Heidelberg, 78.
Geschka, H., Schauffele, J. and Zimmer, C. (2005): Explorative Technologie-
Roadmaps - Eine Methodik zur Erkundung technologischer Entwicklugslinien
und Potenziale. In: M.G. Möhrle and R. Isenmann (Eds.): Technologie-
Roadmapping. Springer, Berlin, Heidelberg et al., 165.
Reiß, T. (2006): Innovationssysteme im Wandel - Herausforderungen für die Innovationspolitik.
In: B. Müller and U. Glutsch (Eds.): Fraunhofer-Institut für
System- und Innovationsforschung - Jahresbericht 2006. Karlsruhe, 10
− 148 −

Multilevel Simultaneous Component Analysis
for Studying Inter-individual and
Intra-individual Variabilities
Marieke E. Timmerman 1 , Anna Lichtwarck-Aschoff 1 , and Eva Ceulemans 2
1
Heymans Institute for Psychology, University of Groningen
Grote Kruisstraat 2/1
9712 TS Groningen, The Netherlands
m.e.timmerman@rug.nl
2
Centre for Methodology of Educational Research, University of Leuven
Belgium
Abstract. All psychological processes are dynamic. To fully understand those processes
it is necessary to consider the intra-individual variation of individuals over
time. Herewith, it is important to recognize that the nature of the processes may
differ across individuals. This intricate matter requires new modelling approaches.
We focus on the exploratory modelling of multivariate data that have been repeatedly
gathered from more than one individual. We aim at identifying meaningful
sources of both the inter-individual variability and the intra-individual variability
in the observed variables, while expressing the similarities and differences in those
sources across individuals. To this end, we use multilevel simultaneous component
analysis (MLSCA; Timmerman, 2006).
In essence, MLSCA specifies separate component models to account for interindividual
and intra-individual variabilities. The latter may entail differences across
individuals, which are expressed via the covariances of the individual’s withincomponent
scores. The common within-loadings ensure the comparability across
individuals. The relationships between MLSCA and the related multilevel and multigroup
structural equation models will be discussed. The usefulness of MLSCA to
grasp inter-individual and intra-individual variabilities is illustrated with an empirical
example from a diary study focusing on emotions involved in daily conflicts
between adolescent girls and their mothers.
Key words: exploratory modelling of longitudinal data, multivariate analysis
References
Timmerman, M.E. (2006). Multilevel Component Analysis. British Journal of Mathematical
and Statistical Psychology, 59, 301–320.
− 149 −

Issues Related to the Implementation of a
Dynamic Logistic Model for Classifier
Combination
Amber Tomas
The University of Oxford, 1 South Parks Road, Oxford OX2 3TG, United
Kingdom tomas@stats.ox.ac.uk
Abstract. We consider a model for classification of sequentially received observations,
when the population of interest is not assumed to be stationary. The model
we propose combines the outputs of a fixed set of component classifiers (chosen in
advance), and the parameters of the combination are allowed to change over time.
Specifically, we use a logistic Dynamic Generalized Linear Model [1] for combining
the classifier outputs, and take a predictive approach towards estimation of the posterior
class probabilities. The dynamics are incorporated through the equation for
parameter evolution
β t+1 = β t + ωt, ωt ∼ N(0, Σt). (1)
The implementation of this model when the distribution of the parameters is
not assumed to be normal is not straightforward. In addition to computational
complexity, there arise complications related to the identifiability of the parameters
β t which are unique to classification problems. Specifically, although the classifications
produced as a result of using the model with parameters β t are equivalent to
the classifications when using parameters αβ t, α > 0, the posterior class probabilities
are more extreme in the second case. This results in increased volatility of the
classification rule when using a sequential MCMC method to estimate the posterior
distribution of the parameters. We discuss why there is no simple constraint for the
parameters which will alleviate this identifiability problem, and discuss an alternative
approach. In addition, we consider the related problems of adaptively changing
the effective value of Σt, and the consequences of using the model (1) when it is not
assumed to be correct.
Key words: Multiple Classifier Systems, Dynamic Classification, Identifiability
References
1.West, M., Harrison, J. and Migon, H. (1985): Dynamic Generalized Linear Models
and Bayesian Forecasting. Journal of the American Statistical Association, 80,
73–83.
− 150 −

A Comprehensive Partial Least Squares
Approach to Component-Based Structural
Equation Modeling ⋆
Laura Trinchera 1 and Vincenzo Esposito Vinzi 2
1
Dipartimento di Matematica e Statistica, Universita degli Studi di Napoli
Federico II. ltrinche@unina.it
2
ESSEC Business School of Paris and Singapore. vinzi@essec.fr
Abstract. PLS Path Modeling (PLS-PM) is generally meant as a componentbased
approach to structural equation modeling that privileges a prediction oriented
discovery process to the statistical testing of causal hypotheses. Differently
from covariance-based structural equation modeling (i.e. LISREL-type methods), in
PLS-PM latent variables are estimated as linear combinations of the manifest variables.
Thus they are more naturally defined as emergent constructs (with formative
indicators) rather than latent constructs (with reflective indicators). Nowadays, formative
relationships are more and more used in real applications but pose a few
problems for the statistical estimation and interpretation. As of today, formative
relationships in PLS-PM imply multiple OLS regressions between each latent variable
and its own formative indicators. As known, OLS regression may yield unstable
results in presence of important correlations between explanatory variables, it is not
feasible when the number of statistical units is smaller than the number of variables
nor when missing data affect the dataset. Thus, it seems quite natural to introduce
a PLS Regression (PLS-R) external estimation mode within the PLS-PM algorithm
so as to overcome the mentioned problems, preserve the formative relationships and
still remain coherent with the component-based and prediction-oriented nature of
PLS-PM. Here, the main issues concerning the use of formative indicators in PLS-
PM are investigated. Furthermore, the features of PLS-R may be fruitfully exploited
in the internal estimation phase as well as for estimating path coefficients upon convergence
of the PLS-PM algorithm when classical OLS estimates become unstable
or even unfeasible. Finally, the case of formative indicators will be considered also
with respect to clustering techniques recently proposed for latent class detection in
PLS-PM.
Key words: Formative Indicators, PLS Regression, Latent Factor Scores
⋆ The participation of L. Trinchera to this research was supported by the MURST
grant “Multivariate statistical models for the ex-ante and the ex-post analysis
of regulatory impact”, coordinated by C. Lauro (2006). The participation of V.
Esposito Vinzi to this research was supported by CERESSEC, Research Center
of the ESSEC Business School.
− 151 −

Relevant Importance of Predictor Variables
in Support Vector Machines Models
Micha̷l Trzesiok
Department of Mathematics,
Katowice University of Economics, ul. Bogucicka 14, 40-226 Katowice
trzesiok@ae.katowice.pl
Abstract. The model resulting from Support Vector Machines suffer from the lack
of interpretation. It is usually very hard to extract the knowledge about the analyzed
phenomenon from the classification model obtained by using SVMs because the
classification task is realized in a high dimensional feature space. Although the
method identifies the observations which are crucial for the form of the decision
function, it does not show which variables are relevant and which are redundant.
Vapnik claims that feature selection is not necessary for SVMs, i.e. building the
model on a set of variables including some redundant variables does not change the
generalization ability. Once the model is built, it is still valuable to recognize the
relative importance of predictor variables. The method we propose uses the sampling
techniques, backward selection and Rand index for evaluating whether the particular
variable is redundant or not. We even try to extend the idea to obtain the ranking
of the predictor variables reflecting the relevant importance of the inputs.
Key words: Support Vector Machines, redundancy, relevant attributes
References
Abe, S. (2005): Support Vector Machines for Pattern Classification, Springer, London.
Breiman, L., Friedman, J., Olshen, R. and Stone, C. (1984): Classification and Regression
Trees, Wadsworth, Monterey.
Schölkof, B. and Smola, A.J. (2002): Learning with Kernels, MIT Press, Cambridge.
Vapnik, V. (1998): Statistical Learning Theory, John Wiley & Sons, N.Y.
− 152 −

Comparison of Algorithms to find differentially
expressed Genes in Microarray Data
Alfred Ultsch
Databionics Research Lab, Department of Computer Science
University of Marburg, D-35032 Marburg, Germany
ultsch@informatik.uni-marburg.de
Summary. There are several different algorithms published for the identification
of differentially expressed genes in DNA microarray experiments. The microarrays
in this type of experiment are from two different populations (groups) of specimen.
Among the many genes on the microarrays, those genes are sought that are the most
relevant for the distinction between the two populations. Usually such algorithms
produce ordered lists of genes. In this work a method to compare the performance
of such algorithms is proposed. In order to compare different methods for the identification
of significant genes, a data set with known properties is published. This
benchmark data is used to compare the performance of different algorithms with a
newly designed one, called PUL. The comparison is based on established measurements
in information retrieval. Surprisingly a clear ordering in performance of the
algorithms was observed. PUL outperformed other algorithms by a factor of two.
PUL was applied successfully in different practical applications. For these experiments
the importance of the genes proposed by PUL were independently verified.
References
1. Gebhard, S., Bergmann, E., Weber, A., Berwanger, B., Eilers, M., Ultsch, A.,
Christiansen, H.: Classification of stage 3 neuroblastomas by artificial neural
networks based analysis of cDNA microarrays. (submitted)
2. Dudoit, S., Fridlyand, J., Speed, T. (2000). Comparison of discrimination methods
for the classification of tumors using gene expression data. Technical report
576, Department of Statistics, University of California, Berkeley.
3. Pallasch C.P., Schwamb J., Schulz A., Knigs S., Debey S., Kofler D., Schultze
J.L., Hallek M., Ultsch, A. Wendtner, C.(2007) Targeting lipid metabolism by
the lipoprotein lipase inhibitor orlistat results in apoptosis in chronic lymphocytic
leukemia, accepted for Leucemia.
4. Tusher, V., Tibshirani, R. and Chu, G. (2001): Significance analysis of microarrays
applied to the ionizing radiation response, PNAS 2001 98: 5116-5121.
5. Ultsch, A.(2005): Improving the identification of differentially expressed genes in
cDNA microarray experiments, In Weihs, C., Gaul, W. (Eds): Classification- the
Ubiquitous Challenge, Springer, Heidelberg, pp. 378-385.
− 153 −

Is log ratio a good value for measuring return
in stock investments?
Alfred Ultsch
Databionics Research Group
Philipps-University of Marburg, Germany
ultsch@informatik.uni-marburg.de
Abstract. Measuring the rate of return is an important issue for theory and practice
of investmets in the stock market. A common measure for rate of return is the
logarithm of the ratio of succesive prices (LogRatio). In this paper it is shown that
LogRatio as well as arithmetic return rate (ROI) have several disadvantages. As an
alternative relative differences (RelDiff) are proposed to measure rates of return.
The stability against numerical and rounding errors of RelDiff is demonstrated
to be much better than for LogRatios and ROI. RelDiff values are identical to
LogRatios and ROI for interesting ranges of return rates. Relative differences map
return rates to a finite range. For most subsequent analyses this is a big advantage.
The usefullness of the approach is demonstrated on daily return rates of a large set
of stocks.
Key words: Rate of Return, Return on Investment, Financial time series, Black-
Scholes-Model
References
Bodie,Z.,KaneA. and Marcus,A.J. Essentials of Investments, 5th Edition. New York:
McGraw-Hill/Irwin, 2004.
Brealey,R. A., Stewart C. Myers,S.C. and Allen,F. Principals of Corporate Finance,
8th Edition. McGraw-Hill/Irwin, 2006.
Feibel,B. J. Investment Performance Measurement. New York: Wiley, 2003.
Franke,J.,Haerdle,W. Hafner,C. Einfhrung in die Statistik der Finanzmaerkte Berlin
u.a. : Springer, 2. Aufl. 2004.
Ultsch, A.: Improving the identification of differentially expressed genes in cDNA
microarray experiments, Weihs, C., Gaul, W. (Eds), In Classification; The Ubiquitous
Challenge, Springer, Heidelberg, (2005), pp. 378-385
Ultsch, A.: Is log ratio a good value for identifying differential expressed genes in
microarray experiments?, Technical Report No. 35, Dept. of Mathematics and
Computer Science, University of Marburg, Germany, (2003)
− 154 −

Mosaic Plots and Knowledge Structures
Ali Ünlü
Department of Mathematics, University of Augsburg, Germany
ali.uenlue@math.uni-augsburg.de
Abstract. Mosaic plots are state-of-the-art graphics for multivariate categorical
data (Hofmann (2008)). Knowledge structures are mathematical models that belong
to the recent theory of knowledge spaces in psychometrics (Doignon and Falmagne
(1999)). This paper presents an application of mosaic plots and variants such as
fluctuations diagrams and multiple barcharts to psychometric data arising from underlying
knowledge structure models. In simulation trials, the scope of this graphing
method in knowledge space theory is investigated.
Key words: Mosaic plot, Visualization, Knowledge structure, Psychometrics
References
Doignon, J.-P. and Falmagne, J.-Cl. (1999): Knowledge Spaces. Springer, Berlin.
Hofmann, H. (2008): Mosaic Plots and Their Variants. In: C.H. Chen, W. Haerdle
and A.R. Unwin (Eds.): Handbook of Data Visualization. Springer, Heidelberg,
617–642.
− 155 −

Visualizing preferences using minimum
variance nonmetric unfolding
Michel van de Velden, Alain de Beuckelaer, Patrick Groenen, and Frank
Busing
No Institute Given
Abstract. In multidimensional unfolding one wishes to obtain a map with subjects
(e.g. consumers) and objects (e.g. products), in such a way that distances
between subjects and objects in the map best represent the preferences as indicated
in the data. Unfolding models are particularly adequate when the data (e.g. consumers’
preferences) are not unidirectional but exhibit an inverted U-shape. If the
alternatives are rated on an interval (or ratio) scale the ‘metric’ unfolding model is
appropriate. If, however, the alternatives are rank ordered or rated on an ordinal
(e.g. Likert-type of) scale, one would need the ’nonmetric’ unfolding model. Until
recently, nonmetric unfolding was not feasible because of degeneracy problems.
Degenerate solutions are solutions were the extent of ‘misfit’ can be made arbitrarily
small Existing algorithms consistently produced such degenerate solutions.
Recently, Busing, Groenen & Heiser (Psychometrika 2005, pp71-98) proposed a solution
to this long-standing methodological problem by including a penalty in the
algorithm. The resulting PREFSCAL algorithm is available in SPSS. In PREFSCAL
two parameters are introduced that determine the strength of the penalty that leads
the algorithm away from degenerate solutions. No clear directions concerning the
choice of the penalty parameters are given. In this paper, we propose a minimum
variance criterion to choose the penalty parameters. By studying the stability of the
unfolding solutions as a function of the penalty parameters, we are able to determine
the penalty in such a way that a minimum variance, non-degenerate solution
is obtained. The data used in our analysis stem from a consumer study in which
consumers were asked to rank-order new product ideas for soups.
− 156 −

Selection of items for tests and questionnaires
using Mokken scale analysis
L. Andries van der Ark and J. Hendrik Straat
Department of Methodology and Statistics
Tilburg University
P.O. Box 90153
5000 LE Tilburg
The Netherlands
a.vdark@uvt.nl
Abstract. Tests or questionnaires are often used to measure personality traits,
attitudes, opinions, skills, and abilities. These tests and questionnaires consist of
questions, statements, problems, games, or rating scales, which are generically called
items. An important step in the construction of a test and questionnaire is a careful
selection of items. A well-known approach for selecting qualitatively good items in a
test is Mokken scale analysis. In this presentation, Mokken scale analysis is explained
and recent developments are discussed. Special attention is given to a comparison
of automated item selection algorithms used in Mokken scale analysis.
− 157 −

Estimating the prevalence of rule transgression
using data collected by randomized response
Peter G.M. van der Heijden
Department of Methodology and Statistics
Utrecht University
PO Box 80140
3508 TC Utrecht
The Netherlands
P.G.M.vanderheijden@uu.nl
Abstract. In criminology self-report studies are a means to obtain prevalence estimates
of for rule transgressions, violations of the law, and so on. In surveys individuals
are interviewed about their behaviour. An obvious problem is, or course,
that due to reasons such as social desirability people do not always answer honestly
about their behaviour.
For this reason about forty years ago randomized response was introduced to
collect data about sensitive issues. Our research group has worked in this area for
about 10 years and I will give an overview of our results. The results are:
• a “best practice” for asking randomized response questions
• a meta-analysis showing that randonized response is the most valid method for
answering questions about sensitive topics
• accommodating existing models for the multivariate data so that they can handle
randomized response data, such as logistic regression, item response theory, and
randmized response count data
• accomodating these models for the potential presence of respondents that do not
follow the randomized response design.
We present these results and illustrate them using surveys that we conducted for the
Ministry of Social Affairs into social benefit fraud, that we conducted on a two-yearly
base from 1998 to 2006.
− 158 −

Clustering Consumers with Respect to Their
Marketing Reactance Behavior
Ralf Wagner and Erik Sauerwald
SVI Chair for International Direct Marketing
DMCC - Dialog Marketing Competence Center
University of Kassel, Germany
rwagner@wirtschaft.uni-kassel.de
erik sauerwald@arcor.de
Abstract. The recent paradigm shift in modern marketing practices (Coviello et
al. (2002), Vargo & Lusch (2004)) in concurrence with the increasing popularity of
digital marketing measures (Wagner & Meißner (forthcoming)) add a new quality
to the discussion of marketing intrusiveness (Morimoto & Chang (2006)).
Despite the comprehensive research in international differences on media usage
(e.g., Krafft et al. (2007)) related previous research frequently neglects the cultural
differences in recipients’ assessment of the marketing measures. In this study we
utilize the Item Response Theory approach for an assessment of individuals’ reactance
to unsolicited marketing communications. The study is based on an survey of
recipients from China, Germany, Russia, and the United Staates of America
Key words: Advertising, Culture, Item Response Theory, Reactance
References
Coviello, N.E., Brodie, R.J., Danaher, P.J., and Johnston, W.J. (2002): How Firms
Relate to Their Markets: An Empirical Examination of Contemporary Marketing
Practices. Journal of Marketing, 66, 33–46.
Krafft, M.; Hesse, J., Höfling, J., Peters, K., Rinas, D. (2007): International Direct
Marketing. Principles, Best Practices, Marketing Facts. Springer, Berlin.
Morimoto, M. and Chang, S. (2006): Consumers’ Attitudes toward Unsolicited Commercial
E-mail and Postal Direct Mail Marketing Methods: Intrusiveness, Perceived
Loss of Control, and Irritation. Journal of Interactive Advertising, 7,
8–20.
Vargo, S.L. and Lusch, R.F. (2004): Evolving to a New Dominant Logic for Marketing.
Journal of Marketing, 68, 1–17.
Wagner, R. and Meißner, M. (forthcoming): Multimedia for Direct Marketing. In:
M. Pagani (Ed.): Encyclopedia of Multimedia Technology and Networking, 2 nd
Edition. Idea Publishing, Hershey.
− 159 −

Supervised Self-Organising Maps and More
Ron Wehrens
IMM, Analytical Chemistry
P.O. Box 9010, 6500 GL Nijmegen
The Netherlands
r.wehrens@science.ru.nl
Abstract. Self-organising maps (SOMs) have been applied in many different areas
of science. In a typical application, large numbers of objects (thousands or more) are
mapped to a two-dimensional grid in such a way that very similar objects end up in
the same area. If several different types of information are available, one can combine
these in one feature vector, used to determine the similarity with each of the map
units, but this presents scaling difficulties. We have, e.g., mapped several thousand
steroid crystal structures from the Cambridge Crystallographic Database, based
on their diffraction patterns and a specific distance function. For these structures,
several other types of information are available as well, such as space group and cell
volume.
To take extra information into account, we have extended the basic principle
of SOMs to accomodate extra layers, one for each type of feature vector [?]. The
closest unit is then determined by summing distances per layer, where each layer
can be assigned a weight. This makes it possible to perform supervised mapping:
the second layer then contains the class information. The result of including class
information is that classes are more likely to form contiguous units in the map. This
behaviour can be enforced by choosing a larger weight for the class information. One
does not have to stop at two layers: it is possible to create several layers, each layer
corresponding with another type of data.
This is implemented in an R package, called “kohonen” [?], available from CRAN
(http://cran.r-project.org). Several examples will be shown highlighting the
possibilities of the technique.
Key words: Self-organising maps, Data fusion, Supervised mapping
References
1.W.J. Melssen, R. Wehrens, and L.M.C. Buydens. Supervised Kohonen networks
for classification problems. Chemom. Intell. Lab. Syst., 83:99–113, 2006.
2.R. Wehrens and L.M.C. Buydens. Self- and super-organising maps in R: the
kohonen package. Journal of Statistical Software, 21(5), 9 2007.
− 160 −

Multi-Item Versus Single-Item Measures:
A Review and Future Research Directions
Petra Wilczynski and Marko Sarstedt
Institute for Market-based Management, Munich School of Management,
D-80539 Munich, Germany wilczynski@bwl.lmu.de
Abstract. With their widely discussed Journal of Marketing Research article,
Bergkvist and Rossiter (2007) resume a long-lasting interdisciplinary discussion on
the benefits and limitations of multi-item versus single-item measures. Whereas
multi-item measures of theoretical constructs have been the norm in marketing research
for over 20 years, practioners seem to favour single-item measures on the
practical grounds of minimizing non-response and costs. This proceeding is often
seen as a fatal error, because single-item measures are believed to be unreliable
and invalid. During the last decades several studies appeared in different disciplines
such as social sciences, marketing or psychology that critically compare these two
approaches, yielding sometimes contradictory results in terms of validity or reliability.
Thus, the objective of this paper is to develop an integrated overview of the
present status of research in this field, taking into account various disciplines. At
this, advantages and disadvantages, analytical approaches as well as the results are
compared and critically evaluated. The findings suggest several areas for future research
in this important field, which is necessary to close the gap between theoretical
and practical requirements.
Key words: Single Item, Multi Item, Scale Development
References
Bergkvist, L. and Rossiter, J.R. (2007): The Predictive Validity of Multiple-Item
Versus Single-Item Measures of the Same Constructs. Journal of Marketing
Research, 44, 175–184.
Drolet, A.L. and Morrison, D.G. (2001): Do We Really Need Multiple-Item Measures
in Service Research? Journal of Service Research, 3, 196–204.
Wanous, J.P. and Reichers, A.E. and Hudy, M.J. (1997): Overall Job Satisfaction:
How Good Are Single-Item Measures? Journal of Applied Psychology, 82,
247–252.
− 161 −

Management and methods: How to do market
segmentation projects
Raimund Wildner
GfK Group
Nürnberg, Germany
Abstract. Market segmentation projects are often strategic projects with top management
attention and high budgets. Nevertheless many of them fail. This can be
due to poor methodology as well as due to poor management.
From the management perspective it is essential that the objectives of the segmentation
have to be clear from the beginning. Product development, media advertising,
or sales are possible objectives and each of them requires specific variables as
an input. Furthermore it is essential that all stakeholders of a segmentation project
are involved from the beginning. During the segmentation project a close cooperation
between marketing experts, market research experts, and statistics expert is
necessary. Special problems arise in international segmentation projects. Finally it
is important to sell the segmentation in the organization by workshops, leaflets and
other instruments that help to get a clear picture of the segments.
From a methodological standpoint it is important that the result is stable so it
can be reproduced in other data sets as well. A test for stability will be shown. Outlier
can cause instability; so a special method to identify outliers will be shown. Faked
interviews have to be excluded from the segmentation. A procedure for detection of
faked interviews will be discussed. Finally the cluster procedure that proved to be
superior in practical terms is discussed.
− 162 −

Clustering with Repulsive Prototypes
Roland Winkler 1 , Frank Rehm 2 , and Rudolf Kruse 3
1 German Aerospace Center, Braunschweig roland.winkler@dlr.de
2 German Aerospace Center, Braunschweig frank.rehm@dlr.de
3 Otto von Guericke University, Magdeburg kruse@iws.cs.uni-magdeburg.de
Abstract. Although there is no exact definition for the term cluster, in the 2D
case, it is fairly easy for human beings to decide which objects belong together. For
machines on the other hand, it is hard to determine which objects form a cluster.
Depending on the problem, the success of a clustering algorithm depends on the
idea of their creators about what a cluster should be. Likewise, each clustering
algorithm comprises a characteristic idea of the term cluster. For example the fuzzy
c-means algorithm tends to find spherical clusters with equal numbers of objects.
Noise clustering focuses on finding spherical clusters of user-defined diameter.
If there is certain amount of knowledge available about how clusters are shaped, it
is possible to include more information into a clustering algorithm. In this paper, we
present an extension to noise clustering that tries to maximize the distances between
prototypes. For that purpose, the prototypes behave like repulsive magnets that
have an inertia depending on their sum of membership values. Using this repulsive
extension, it is possible to prevent that groups of objects are divided into more
than one cluster. Due to the repulsion and inertia, it is also possible to determine
the number of clusters in a data set. Roughly speaking, having information about
cluster shapes (i.e. the diameter) may help to cope with the absence of knowledge
concerning the exact number of clusters.
The results of repulsive clustering can be used as an initialization for other clustering
techniques. We successfully applied this method on air traffic management tasks.
Key words: fuzzy clustering, cluster shapes, cluster validity, air traffic management
References
Bezdek, J.C.(1981): Pattern recognition with fuzzy objective function algorithms.
Plenum Press, New York.
Dave RN, Sumit S (1998): Generalized noise clustering as a robust fuzzy c-mestimators
model. 17th Annual Conference of the North American Fuzzy Information
Processing Society (NAFIPS-98), Pensacola Beach, Florida, 256–260
Rehm, F., Klawonn, F., Kruse, R.(2007): A novel approach to noise clustering for
outlier detection. Soft Computing - A Fusion of Foundations, Methodologies and
Applications, Berlin/Heidelberg, Vol. 11, No 5, 489-494.
− 163 −

On the Effects of Enhanced Selection Models
on Quality and Comparability of Classifiers
Produced by Genetic Programming
Stephan Winkler, Michael Affenzeller, Stefan Wagner, and Gabriel
Kronberger
Fachhochschule Oberösterreich, Research Center Hagenberg
{swinkler,maffenze,swagner,gkronber}@heuristiclab.com
Abstract. The use of genetic programming (GP) in machine learning enables the
automated search for classification models that are evolved by an evolutionary process
using the principles of selection, crossover and mutation. The use of enhanced
selection models in GP ([1], [2]) is able to significantly increase the quality of classifiers
produced by GP; detailed analysis can be found for example in [3].
Algorithmic reliability can be assessed by comparing the results produced by a
machine learning algorithm; due to the stochastic element that is intrinsic to any
evolutionary process, GP can not guarantee the generation of similar or even similar
models in each GP process execution. In [4] we have presented a method how to
compare time series models produced by GP; in this paper we analyze the classifiers
returned by GP based machine learning for medical benchmark data sets (taken
from the UCI repository). We mainly focus on comparing standard GP techniques
to those using enhanced selection models with respect to results similarity analysis.
The effects of pruning mechanisms (applied to the final results) are also discussed.
Key words: Evolutionary Learning, Genetic Programming, Results Comparability
References
[1] Affenzeller, M. and Wagner, S. (2005): Offspring selection: A new self-adaptive
selection scheme for genetic algorithms. Adaptive and Natural Computing Algorithms,
218—221.
[2] Wagner, S. and Affenzeller, M. (2005): SexualGA: Gender-Specifc Selection for
Genetic Algorithms. Proceedings of the 9th World Multi-Conference on Systemics,
Cybernetics and Informatics (WMSCI) 2005, 4: 76–81.
[3] Winkler, S., Affenzeller, M. and Wagner, S. (2007): Advanced genetic programming
based machine learning. Journal of Mathematical Modelling and Algorithms,
6(3): 455—480. Springer, Berlin.
[4] Winkler, S., Affenzeller, M. and Wagner, S. (2008): On the Reliability of Nonlinear
Modeling Using Enhanced Genetic Programming Techniques. Proceeding of
the Chaotic Modeling and Simulation International Conference CHAOS 2008.
− 164 −

Analysis of massive emigration from Poland –
the model–based clustering approach
Ewa Witek
Department of Statistics,
Katowice University of Economics, Bogucicka 14, 40–226 Katowice
ewitek@ekonom.ae.katowice.pl
Abstract. The model–based approach assumes that data is generated by a finite
mixture of underlying probability distribution such as multivariate normal distribution.
In finite mixture models, each component of probability distribution corresponds
to a cluster. The problem of determining the number of clusters and choosing
an appropriate clustering method becomes statistical model choice problem. Hence,
the model–based approach provides a key advantage over heuristic clustering algorithms
selecting both the correct model and the number of clusters.
Model–based clustering has shown promise in a number of practical applications,
including tissue segmentation, character recognition, minefield and seismic fault detection
and classification of astronomical data. The article presents an application
of the model–based clustering in economic analysis, which is comparatively rare.
The moment Poland joined the EU, its citizens rushed out of the country. Since
1 May 2004 Poland has been facing the problem of increased emigration. We used
the model–based clustering approach for grouping and detecting inhomogeneities of
Polish emigrants from different regions of Poland.
Key words: Model–based clustering, EM algorithm, BIC
References
Fraley, C. and Raftery, A.E. (2002): Model–based clustering, discriminant analysis,
and density estimation. Journal of the American Statistical Association, 97,
611–631.
McLachlan, G.J. and Peel, D. (2000): Finite mixture models. Willey, New York.
− 165 −

Image Based Mail Piece Identification using
Unsupervised Learning
Katja Worm 1 and Beate Meffert 2
1 Siemens ElectroCom Postautomation GmbH, Rudower Chaussee 29, 12489
Berlin, Germany katja.worm@siemens.com
2 Humboldt Universität zu Berlin, Rudower Chaussee 25, 12489 Berlin, Germany
meffert@informatik.hu-berlin.de
Abstract. Based on the uniqueness of a mail piece surface, postal sorting machines
use mail piece image characteristics to reuse once extracted mail piece addresses in
different sorting steps. During the first sorting step mail piece image characteristics
are extracted and stored together with its target address in a database. In following
sorting steps the mail piece address is accessed by determining the corresponding
mail piece characteristics in the database. In a previous work appropriate mail piece
image characteristics and procedures for their distance measurement were presented.
Image based mail piece identification is complicated by a constantly changing
and unknown mail piece spectrum as well as the differentiation of nearly identical
mass mails. In particular, the rejection of unknown mail pieces requires the definition
of specific rejection classes depending on the current mail piece spectrum.
In this paper we present an approach on distance based mail piece identification
using a two-stage classification process. The different handling of mass and collection
mail is facilitated by a beforehand performed unsupervised learning process which
clusters similar mail piece characteristics. Based on theses clusters a specific rejection
class can be estimated within each cluster. The first step in the identification process
is the determination of the corresponding cluster for a given mail piece. Based on
the cluster specific rejection class the mail piece can be either identified or rejected.
Experimental results obtained on real-world data sets show the applicability of the
proposed method.
References
Worm, K. and Meffert, B. (2008): Robust Image Based Document Comparison Using
Attributed Relational Graphs. Proceedings of the International Conference on
Signal Processing, Pattern Recognition and Applications (SPPRA), accepted.
Keywords
DOCUMENT IDENTIFICATION, UNSUPERVISED LEARNING,
MINIMUM DISTANCE CLASSIFICATION
− 166 −

Factor Analysis of Incomplete Disjunctive
Tables
Amaya Zárraga 1 and Beatriz Goitisolo 1
Departamento de Economía Aplicada III. UPV/EHU. Bilbao. Spain
Amaya.Zarraga@ehu.es and Beatriz.Goitisolo@ehu.es
Abstract. Multiple Correspondence Analysis (MCA) studies the relationship between
several categorical variables defined with respect to a certain population.
However, one of the main sources of information are those surveys in which it is
usual to find a certain number of absent data and conditioned questions that do not
need to be answered by the whole population. In these cases, the data codification in
a complete disjunctive table requires the inclusion of non-answer categories that can
alter the results. For example, the distance χ 2 between two row profiles increases
with the common answers when the individuals do not answer the same number
of questions. And in the distance χ 2 between two column profiles each individual
could have a different weight according to the number of answers previously chosen.
Therefore, the direct application of the standard MCA is not appropriate to
the study of an incomplete disjunctive table (IDT). We propose the analysis of the
incomplete disjunctive table by substituting the real marginal of the table about the
individuals for a suitable imposed marginal.
Key words: Multiple Correspondence Analysis, Complete Disjunctive Tables, Incomplete
Disjunctive Tables
References
Zárraga, A. and Goitisolo, B. (1999): Independence Between Questions in the Factor
Analysis of Incomplete Disjunctive Tables with Condicioned Questions.
Qüestiió, vol 23, 3, 465–488.
Zárraga, A. and Goitisolo, B. (2008): Factorial Analysis of a set of Contingency Tables.
In: C. Preisach, H. Burkhardt, L. Schmidt-Thieme and R. Decker (Eds.):
Data Analysis, Machine Learning and Applications Series: Studies in Classification,
Data Analysis, and Knowledge Organization. Proceedings of the 31st
Annual Conference of the Gesellschaft fr Klassifikation e.V., Albert-Ludwigs-
Universitt Freiburg, March 7-9, 2007. Springer, Berlin, forthcoming.
− 167 −

Recursive Partitioning of Economic
Regressions: Trees of Costly Journals and
Beautiful Professors
Achim Zeileis 1 and Christian Kleiber 2
1 Department of Statistics, Department of Statistics and Mathematics,
Wirtschaftsuniversität Wien Achim.Zeileis@wu-wien.ac.at
2 Wirtschaftswissenschaftliches Zentrum, Universität Basel
Christian.Kleiber@unibas.ch
Abstract. The linear regression model is the workhorse for empirical economic
analyses. For a wide variety of standard analysis problems, there are useful specifications
of linear regression models, validated by economic theory and prior successful
empirical studies. However, in non-standard problems or in situations where data on
additional variables is available, a useful specification of a regression model involving
all variables of interest might not be available. Here, we explore how recursive
partitioning techniques can be used in such situations for modeling the relationship
between the dependent variable and the available regressors. Linear regression is
embedded into the model-based recursive partitioning framework of Zeileis et al.
(2008). The resulting regression trees are grown by recursively applying techniques
for testing and dating structural changes in linear regressions. They are compared
to classical modeling approaches in two empirical applications: Following Stock and
Watson (2007), the demand for economic journals (Bergstrom, 2001) is investigated.
Furthermore, the impact of professors’ beauty on their class evaluations (Hamermesh
and Parker, 2005) is assessed.
Key words: Regression trees, Model-based recursive partitioning, Structural change
References
Bergstrom, T.C. (2001): Free Labor for Costly Journals? Journal of Economic Perspectives,
(15), 183–198.
Hamermesh, D.S. and Parker, A. (2005): Beauty in the Classroom: Instructors’
Pulchritude and Putative Pedagogical Productivity. Economics of Education
Review, 24, 369–376.
Stock, J.H. and Watson, M.W. (2007): Introduction to Econometrics. 2nd edition,
Addison Wesley.
Zeileis, A., Hothorn, T. and Hornik, K. (2008): Model-based Recursive Partitioning.
Journal of Computational and Graphical Statistics, accepted for publication.
− 168 −