Technical Sessions – Monday July 11

3 - Mathematical Models for Management of Water Supply
and Distribution
Julia Piantadosi, School of Mathematics and Statistics,
University of South Australia, Mawson Lakes Campus, Mawson
Lakes Boulevard, Mawson Lakes, 5095, Adelaide, South
Australia, julia.piantadosi@unisa.edu.au
For a given (stochastic) level of supply it is important to manage water distribution
in an optimal and sustainable way. We discuss applications of stochastic
dynamic programming (SDP) using Conditional Value-at-Risk (CVaR) rather
than Expected Monetary Value as an objective. It may be preferable to minimize
environmental impact when untoward events occur rather than maximizing
financial returns. By valuation of environmental assets one may quantify
CVaR and hence SDP can be used to select a risk-averse policy. By adjusting
key parameters we can investigate a range of climate-change scenarios.
� FB-09
Friday, 13:15-14:45
Meeting Room 108
Data Mining
Stream: Contributed Talks
Contributed session
Chair: Jung Ha Seo, Global Production Technology Center, Samsung
Electronics, 416, Maetan-3Dong, Yeongtong-Gu, 443-742, Suwon,
Gyeonggi-Do, Korea, Republic Of, jung2010@korea.ac.kr
1 - Spam Filtering with Generalized Additive Neural Networks
Tiny Du Toit, Computer Science and Information Systems,
North-West University, 11 Hoffman Street, 2531, Potchefstroom,
North-West, South Africa, Tiny.DuToit@nwu.ac.za
The number of spam messages sent has increased significantly during the last
decade. These unsolicited emails place a heavy burden on end users and email
service providers. For this presentation a Generalized Additive Neural Network
(GANN) is utilized to detect spam. GANNs have a number of strengths that
makes them a suitable classifier of spam. An automated GANN construction
algorithm is applied to a spam data set. Results obtained compare favourable
to other classifiers found in the literature and can be interpreted by graphical
methods.
2 - Spatial Data Mining in Municipal Content Management
Systems
Ronny Weinkauf, Informatik und Kommunikationssysteme,
Hochschule Merseburg, Geusaer Str., 06217, Merseburg,
Germany, ronny.weinkauf@hs-merseburg.de
Target of this project is a recommendation system as a module of a content
management system. It can be used to improve the usability of online platforms
by offer content cross references. In a first step the enhanced content management
system logs user and content interactions of municipal online platforms
and storing it. Before the data is processed by common association algorithms,
it will be enriched with geographical information and content categories. The
association algorithms will produce rules which can be used to create automatically
a list of recommendations.
3 - Design of Customized Promotions Supported by Data
Mining Techniques Applied to a Loyalty Card Database
Vera Miguéis, DEIG, Faculdade de Engenharia da Universidade
do Porto, Porto, Portugal, vera.migueis@fe.up.pt, Ana Camanho,
João Cunha
A good relationship between companies and customers is a critical factor of
competitiveness. The design of customized promotions has gained prominence
as a marketing tool to enforce loyalty relationships. In this context, we propose
a method to support customer-oriented marketing policies, based on transaction
records stored in a loyalty card database of a European retailing company
used as case study. The data mining techniques used include cluster analysis
and decision trees to segment customers and market basket analysis to identify
the product subcategories usually purchased together.
4 - Time-invariant Feature Selection for Multivariate Timeseries
Data
Jung Ha Seo, Global Production Technology Center, Samsung
Electronics, 416, Maetan-3Dong, Yeongtong-Gu, 443-742,
IFORS 2011 - Melbourne FB-10
Suwon, Gyeonggi-Do, Korea, Republic Of,
jung2010@korea.ac.kr, Cheong Sool Park, Sung-Shick Kim,
Jun-Geol Baek
For Multivariate time-series (MTS) data analysis, multivariate auto-regressive
and moving average (ARMA) was applied. But multivariate ARMA is not appropriate
to detect the time point to match with time-invariant feature. The
time point of time-invariant feature can figure out faults or start of class-toclass
change of classification problems in real-time process. So, we propose an
algorithm for detecting time-invariant feature in MTS. The algorithm consists
of feature generation using wavelets, extraction important feature vector from
MTS and matching time point with time-invariant feature.
� FB-10
Friday, 13:15-14:45
Meeting Room 111
Stochastic Optimisation
Stream: Contributed Talks
Contributed session
Chair: Marlin Thomas, Grad School of Engr & Mgmt, Air Force
Institute of Technology, 2950 Hobson Way, 45433-7765,
Wright-Paterson AFB, OH, United States, marlin.thomas@afit.edu
1 - Local Convergence of Interior Point Methods on Semidefinite
Programs using a Path-Based Approach
Chee Khian Sim, Department of Applied Mathematics, The
Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong
Kong, macksim@inet.polyu.edu.hk
We first define a semi-definite program (SDP), and state a global convergence
result using an interior point algorithm to solve it. Then we define certain paths
which can be used to analyze the local convergence behavior of the algorithm
when solving the SDP. Using the path-based approach, we give a sufficient
condition for superlinear convergence of such an algorithm when solving an
SDP. Based on this sufficient condition, we derive a condition for superlinear
convergence on the class of semi-definite linear feasibility problems using the
algorithm.
2 - Solving Two-stage Stochastic Quadratic Problems using
the TNF Strategy
Eugenio Mijangos, Applied Mathematics and Statistics and
Operations Research, UPV/EHU, P.O. Box 644 – Dept.
Matematica Aplicada y E.I.O. (UPV/EHU), 48080, Bilbao,
Spain, eugenio.mijangos@ehu.es
We present an algorithm to solve two-stage stochastic quadratic (TSSQ) problems.
It is based on the Twin Node Family (TNF) concept involved in the
Branch-and-Fix Coordination method. These problems have continuous and
binary variables in the first stage and only continuous variables in the second
stage. The objective function is quadratic and the constraints are linear. On
the basis that the nonanticipativity constraints are fulfilled by TNF strategy, an
algorithm to solve TSSQ problems is designed and implemented using Cplex
to solve the QP subproblems. Numerical results are reported.
3 - Dynamic Choice Theory and Dynamic Consistency of
Risk Measures
Jean-Philippe Chancelier, CERMICS Ecole des Ponts et
Chaussées, Université Paris Est, 6 et 8 Av Blaise Pascal,Cite
Descartes |, Champs sur Marne, 77455, Marne La vallee cedex
02, France, jpc@cermics.enpc.fr
In a seminal article Kreps and Porteus study three axioms on preferences and
show that these axioms are equivalent to the existence of a utility function.
They also study, in a dynamic choice framework, how to tie together preferences
at different times by giving a temporal consistency axiom. They derive
from the consistency axiom a representation theorem for dynamic utility functions.
We make links between their original work and temporal consistency
axioms found in risk measure theory and to derive the representation for dynamic
risk measures.
4 - Data Driven versus Structure Driven Markov Chain
Modeling
Marlin Thomas, Grad School of Engr & Mgmt, Air Force
Institute of Technology, 2950 Hobson Way, 45433-7765,
119