TESI DOCTORAL - La Salle

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A.2. Data sets Strategy Similarity cos i1 i2 • e1 • h1 g1 Criterion function g1p slink wslink clink wclink upgma rb √ × × × × × × × corr dist × • × • × × × × × × × × × × × × × × × × jacc × × × × × × × × × × × cos • • × × × × × × × rbr corr dist × • × • × × × × × × × × × × × × × × × × jacc × × × × × × × × × × × cos • • × × × × × × × direct corr dist • × × • × × × × × × × × × × × × × × × × jacc × × × × × × × × × × × cos × × × × × × • • • agglo corr dist × × × × × × × × × × × × • × × • × × • × jacc × × × × × × × × × × × cos × × × × × × • • • bagglo corr dist × × × × × × × × × × × × • × × • × × • × jacc × × × × × × × × × × × cos • • × × × × × graph corr dist × × × × × × × × × × jacc • • × × × × × Table A.1: Cross-option table indicating which clustering strategy-criterion functionsimilarity measure combinations are allowed (), not allowed (×) and employed in our experiments (•). 220
A.2.1 Unimodal data sets Appendix A. Experimental setup A total of twelve unimodal data sets have been used in the experimental sections of this thesis. Unless noted otherwise, these data sets have been obtained from two classic public data repositories for the data mining and machine learning research communities such as the UCI Knowledge Discovery in Databases Archive (Hettich and Bay, 1999) and the UCI Machine Learning Repository (Asuncion and Newman, 1999). In the following paragraphs, we present a brief description of each data set, summarizing their most relevant characteristics in table A.2 as a quick reference source. 1. Zoo: the goal of this data set is to learn to classify animals into seven classes given 17 binary attributes representing features such as the presence of hair, feathers, backbone or teeth, or whether it is an aquatic or airborne animal, among others. The number of objects (i.e. animals) in the data set is 101. 2. Iris: a classic data set in machine learning and pattern recognition. It contains 150 objects (instances of Iris plants) represented by four real-valued features measuring the width and length of its petals and sepals. The goal is to classify the objects into one of the three classes of Iris plants, one of which is linearly separable from the others, while the latter two are not linearly separable from each other. 3. Wine: this data set’s goal is to determine the origin of wines by means of chemical analysis. It contains 178 samples of wine which must be categorized into three wine classes based on their contents of 13 constituents such as alcohol, malic acid, or magnesium, represented as real-valued features. 4. Glass: in this data set, 214 instances of glass are represented by 10 real-valued attributes corresponding to their contents in chemical elements such as aluminium, sodium, calcium, etc. The goal is to classify the objects into one of the predefined six categories (types of glass). 5. Ionosphere: the contents of this data set are 351 radar returns from the ionosphere, classified either as good or bad depending on whether they show evidence of some type of structure in the ionosphere or not. Each radar return is described by 34 autocorrelation-based real-valued features. 6. WDBC : its complete name is Wisconsin Diagnostic Breast Cancer data set. It contains 569 objects (breast mass images) represented by 32 real-valued features describing characteristics of the cell nuclei present in the image (radius, texture, perimenter, etc.). The goal is to classify these objects into one of the possible cancer diagnostics (malignant or benign). 7. Balance: this data set was generated to model psychological experimental results. Each of the 625 objects is classified into three classes (as having the balance scale tip to the right, tip to the left, or balanced). The integer-valued attributes are the left weight, the left distance, the right weight, and the right distance. 8. Mfeat: its original name is Multiple Features data set, as it represents the objects it contains (handwritten numerals from 0 to 9) using different real-valued features such as Fourier coefficients, profile correlations, Karhunen-Loève coefficients, pixel averages, Zernike moments and morphological attributes. 221
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C.I.F. G: 59069740 Universitat Ramo
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Resum En segmentar de forma no supe
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Abstract When facing the task of pa
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Contents 2.2.6 Consensus functions
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Contents A.5 Consensus functions .
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Contents D.2.5 WDBC data set . . .
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List of Tables 3.13 Relative percen
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List of Tables 5.15 Relative φ (NM
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List of Figures 1.1 Evolution of th
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List of Figures 4.2 Decreasingly or
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List of Figures C.18 Estimated and
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List of Figures C.49 φ (NMI) of th
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List of Figures D.22 φ (NMI) boxpl
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List of Algorithms 6.1 Symbolic des
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List of symbols OΛ: object co-asso
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Chapter 1. Framework of the thesis
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1.1. Knowledge discovery and data m
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1.1. Knowledge discovery and data m
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1.2. Clustering in knowledge discov
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1.3. Multimodal clustering in clust
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1.4. Clustering indeterminacies exe
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1.4. Clustering indeterminacies The
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1.4. Clustering indeterminacies Dat
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1.5. Motivation and contributions o
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Chapter 2. Cluster ensembles and co
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fuzzy consensus clustering solution
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2.1. Related work on cluster ensemb
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2.2. Related work on consensus func
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3.1. Motivation - the computational
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3.1. Motivation An additional and v
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3.2. Random hierarchical consensus
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3.5. Discussion Consensus Consensus
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3.5. Discussion separate clustering
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Chapter 4 Self-refining consensus a
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Chapter 4. Self-refining consensus
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- What do we want to measure? Chapt
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φ (NMI) φ (NMI) φ (NMI) φ (NMI)
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φ (NMI) 0.8 0.78 0.76 0.74 0.72 0
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1. Given a cluster ensemble E conta
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%of experiments relative % φ (NMI)
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Publisher: Springer Series: Lecture
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5.1. Generation of multimodal clust
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5.2. Self-refining multimodal conse
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5.3. Multimodal consensus clusterin
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5.4. Discussion Data set Relative
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5.5. Related publications modes joi
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Chapter 6. Voting based consensus f
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6.2. Adapting consensus functions t
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6.2. Adapting consensus functions t
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Page 243 and 244: References Fred, A. and A.K. Jain.
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Page 247 and 248: References Li, S.Z. and G. GuoDong.
Page 249 and 250: References Sebastiani, F. 2002. Mac
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D.2. Experiments on selection-based
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E.1. CAL500 data set φ (NMI) 1 0.8
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E.1. CAL500 data set φ (NMI) CSPA
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E.2. InternetAds data set φ (NMI)
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E.3. Corel data set φ (NMI) 1 0.8
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F.1. Iris data set φ (NMI) 1 0.8 0
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F.9. Segmentation data set φ (NMI)
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F.10. BBC data set φ (NMI) 1 0.8 0
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F.11. PenDigits data set φ (NMI) 1
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