TESI DOCTORAL - La Salle

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Contents D.2.5 WDBC data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 351 D.2.6 Balance data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 351 D.2.7 MFeat data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 352 D.2.8 miniNG data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353 D.2.9 Segmentation data set . . . . . . . . . . . . . . . . . . . . . . . . . . 353 D.2.10 BBC data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 354 D.2.11 PenDigits data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . 355 E Experiments on multimodal consensus clustering 359 E.1 CAL500 data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 359 E.1.1 Consensus clustering per modality and across modalities . . . . . . . 360 E.1.2 Self-refined consensus clustering across modalities . . . . . . . . . . . 362 E.2 InternetAds data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 364 E.2.1 Consensus clustering per modality and across modalities . . . . . . . 364 E.2.2 Self-refined consensus clustering across modalities . . . . . . . . . . . 365 E.3 Corel data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 366 E.3.1 Consensus clustering per modality and across modalities . . . . . . . 366 E.3.2 Self-refined consensus clustering across modalities . . . . . . . . . . . 369 F Experiments on soft consensus clustering 373 F.1 Iris data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 374 F.2 Wine data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 374 F.3 Glass data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 375 F.4 Ionosphere data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 377 F.5 WDBC data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 377 F.6 Balance data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 379 F.7 MFeat data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 380 F.8 miniNG data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 381 F.9 Segmentation data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 382 F.10 BBC data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 384 F.11 PenDigits data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 385 xvi
List of Tables 1.1 Illustration of the clustering algorithm indeterminacy on the BBC and PenDigits data sets clustered by the direct-cos-i2 and graph-cos-i2 algorithms . . . 23 1.2 Illustration of the clustering indeterminacies on the CAL500, Corel, InternetAds and IsoLetters multimoda data sets . . . . . . . . . . . . . . . . . . . 24 2.1 Taxonomy of consensus functions according to their theoretical basis . . . . 34 3.1 Number of inner loop iterations as a function of the outer’s loop index i . . 53 3.2 Methodology for estimating the running time of multiple RHCA variants . 56 3.3 Evaluation of the minimum complexity RHCA variant estimation methodology in terms of the percentage of correct predictions and running time penalizations resulting from mistaken predictions . . . . . . . . . . . . . . . 63 3.4 Computationally optimal consensus architectures (flat or RHCA) on the unimodal data collections assuming a fully serial implementation . . . . . . . . 67 3.5 Computationally optimal consensus architectures (flat or RHCA) on the unimodal data collections assuming a fully parallel implementation . . . . . . . 68 3.6 Methodology for estimating the running time of multiple DHCA variants . 74 3.7 Evaluation of the minimum complexity DHCA variant estimation methodology in terms of the percentage of correct predictions and running time penalizations resulting from mistaken predictions . . . . . . . . . . . . . . . 81 3.8 Evaluation of the minimum complexity serial DHCA variant prediction based on decreasing diversity factor ordering in terms of the percentage of correct predictions and running time penalizations resulting from mistaken predictions 83 3.9 Running time differences between the most and least computationally efficient DHCA variants in both the serial and parallel implementations . . . . 84 3.10 Computationally optimal consensus architectures (flat or DHCA) on the unimodal data collections assuming a fully serial implementation . . . . . . . . 86 3.11 Computationally optimal consensus architectures (flat or DHCA) on the unimodal data collections assuming a fully parallel implementation . . . . . . . 87 3.12 Percentage of experiments in which the consensus clustering solution is better than the median cluster ensemble component . . . . . . . . . . . . . . . . . 104 xvii
Page 1: C.I.F. G: 59069740 Universitat Ramo
Page 5: Resum En segmentar de forma no supe
Page 9: Abstract When facing the task of pa
Page 12 and 13: Contents 2.2.6 Consensus functions
Page 14 and 15: Contents A.5 Consensus functions .
Page 18 and 19: List of Tables 3.13 Relative percen
Page 20 and 21: List of Tables 5.15 Relative φ (NM
Page 23 and 24: List of Figures 1.1 Evolution of th
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Page 27 and 28: List of Figures C.18 Estimated and
Page 29 and 30: List of Figures C.49 φ (NMI) of th
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Page 33: List of Algorithms 6.1 Symbolic des
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Page 38 and 39: Chapter 1. Framework of the thesis
Page 40 and 41: 1.1. Knowledge discovery and data m
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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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3.3. Deterministic hierarchical con
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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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6.3. Voting based consensus functio
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6.4. Experiments λc = 1 1 1 3 3 3
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6.4. Experiments Data set Soft clus
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6.4. Experiments CSPA EAC HGPA MCLA
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6.5. Discussion solutions on hard c
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Chapter 7 Conclusions The contribut
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Chapter 7. Conclusions of-the-art c
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Chapter 7. Conclusions Though put f
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Chapter 7. Conclusions clustering r
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Chapter 7. Conclusions hardened. Ou
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References Ben-Hur, A., D. Horn, H.
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References Deerwester, S., S.-T. Du
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References Fred, A. and A.K. Jain.
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References Ingaramo, D., D. Pinto,
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References Li, S.Z. and G. GuoDong.
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References Sebastiani, F. 2002. Mac
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References Topchy, A., A.K. Jain, a
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Appendix A Experimental setup A.1 T
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Appendix A. Experimental setup g. s
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A.2.1 Unimodal data sets Appendix A
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A.2.2 Multimodal data sets Appendix
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Appendix A. Experimental setup gene
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Appendix A. Experimental setup orig
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Appendix A. Experimental setup Data
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Appendix A. Experimental setup or N
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B.1. Clustering indeterminacies in
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C.1. Configuration of a random hier
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C.2. Estimation of the computationa
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C.4. Computationally optimal RHCA,
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D.1. Experiments on consensus-based
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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.3. Glass data set CSPA EAC HGPA M
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F.5. WDBC data set φ (NMI) 1 0.8 0
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F.7. MFeat data set φ (NMI) 1 0.8
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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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