TESI DOCTORAL - La Salle

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Contents A.5 Consensus functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229 A.6 Computational resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 230 B Experiments on clustering indeterminacies 233 B.1 Clustering indeterminacies in unimodal data sets . . . . . . . . . . . . . . . 233 B.1.1 Zoo data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234 B.1.2 Iris data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235 B.1.3 Wine data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 236 B.1.4 Glass data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 236 B.1.5 Ionosphere data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237 B.1.6 WDBC data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237 B.1.7 Balance data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 238 B.1.8 MFeat data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 238 B.1.9 miniNG data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 238 B.1.10 Segmentation data set . . . . . . . . . . . . . . . . . . . . . . . . . . 238 B.1.11 BBC data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239 B.1.12 PenDigits data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . 240 B.1.13 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241 B.2 Clustering indeterminacies in multimodal data sets . . . . . . . . . . . . . . 241 B.2.1 CAL500 data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243 B.2.2 Corel data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243 B.2.3 InternetAds data set . . . . . . . . . . . . . . . . . . . . . . . . . . . 244 B.2.4 IsoLetters data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245 B.2.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246 C Experiments on hierarchical consensus architectures 249 C.1 Configuration of a random hierarchical consensus architecture . . . . . . . . 249 C.2 Estimation of the computationally optimal RHCA . . . . . . . . . . . . . . 252 C.2.1 Iris data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253 C.2.2 Wine data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253 C.2.3 Glass data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 256 C.2.4 Ionosphere data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . 256 C.2.5 WDBC data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261 C.2.6 Balance data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261 C.2.7 MFeat data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266 C.3 Estimation of the computationally optimal DHCA . . . . . . . . . . . . . . 271 C.3.1 Iris data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272 xiv
Contents C.3.2 Wine data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273 C.3.3 Glass data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273 C.3.4 Ionosphere data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . 278 C.3.5 WDBC data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 278 C.3.6 Balance data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283 C.3.7 MFeat data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283 C.4 Computationally optimal RHCA, DHCA and flat consensus comparison . . 290 C.4.1 Iris data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 290 C.4.2 Wine data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 291 C.4.3 Glass data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295 C.4.4 Ionosphere data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . 301 C.4.5 WDBC data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306 C.4.6 Balance data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306 C.4.7 MFeat data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 314 C.4.8 miniNG data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 314 C.4.9 Segmentation data set . . . . . . . . . . . . . . . . . . . . . . . . . . 318 C.4.10 BBC data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 324 C.4.11 PenDigits data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . 324 D Experiments on self-refining consensus architectures 333 D.1 Experiments on consensus-based self-refining . . . . . . . . . . . . . . . . . 333 D.1.1 Iris data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 334 D.1.2 Wine data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 334 D.1.3 Glass data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 334 D.1.4 Ionosphere data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337 D.1.5 WDBC data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337 D.1.6 Balance data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337 D.1.7 MFeat data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 342 D.1.8 miniNG data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 342 D.1.9 Segmentation data set . . . . . . . . . . . . . . . . . . . . . . . . . . 342 D.1.10 BBC data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 342 D.1.11 PenDigits data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . 346 D.2 Experiments on selection-based self-refining . . . . . . . . . . . . . . . . . . 348 D.2.1 Iris data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 348 D.2.2 Wine data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 349 D.2.3 Glass data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 349 D.2.4 Ionosphere data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . 350 xv
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
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Page 18 and 19: List of Tables 3.13 Relative percen
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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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6.3. 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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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.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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