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E.3. Corel data set φ (NMI) 1 0.8 0.6 0.4 0.2 0 CSPA graph−cos−i2 E λc λ 2 c λ 5 c λ 10 c λ 15 c λ 20 c (a) CSPA φ (NMI) λ 30 c 1 0.8 0.6 0.4 0.2 0 λ 40 c λ 50 c λ 75 c φ (NMI) 1 0.8 0.6 0.4 0.2 ALSAD graph−cos−i2 E λc λ 2 c λ 5 c λ 10 c λ 15 c λ 20 c 0 λ 30 c (e) ALSAD λ 40 c EAC graph−cos−i2 E λc λ 2 c λ 5 c λ 10 c λ 15 c λ 20 c (b) EAC λ 50 c λ 75 c φ (NMI) λ 30 c 1 0.8 0.6 0.4 0.2 0 λ 40 c λ 50 c λ 75 c φ (NMI) 1 0.8 0.6 0.4 0.2 KMSAD graph−cos−i2 E λc λ 2 c λ 5 c λ 10 c λ 15 c λ 20 c 0 λ 30 c (f) KMSAD λ 40 c HGPA graph−cos−i2 E λc λ 2 c λ 5 c λ 10 c λ 15 c λ 20 c (c) HGPA λ 50 c λ 75 c φ (NMI) λ 30 c 1 0.8 0.6 0.4 0.2 0 λ 40 c λ 50 c λ 75 c φ (NMI) 1 0.8 0.6 0.4 0.2 SLSAD graph−cos−i2 E λc λ 2 c λ 5 c λ 10 c λ 15 c λ 20 c 0 λ 30 c (g) SLSAD MCLA graph−cos−i2 E λc λ 2 c λ 5 c λ 10 c λ 15 c λ 20 c (d) MCLA Figure E.15: φ (NMI) boxplots of the self-refined intermodal consensus clustering solutions using the graph-cos-i2 algorithm on the InternetAds data set. φ (NMI) 1 0.8 0.6 0.4 0.2 0 CSPA rb−cos−i2 E λc λ 2 c λ 5 c λ 10 c λ 15 c λ 20 c (a) CSPA φ (NMI) λ 30 c 1 0.8 0.6 0.4 0.2 0 λ 40 c λ 50 c λ 75 c φ (NMI) 1 0.8 0.6 0.4 0.2 0 ALSAD rb−cos−i2 E λc λ 2 c λ 5 c λ 10 c λ 15 c λ 20 c λ 30 c (e) ALSAD λ 40 c EAC rb−cos−i2 E λc λ 2 c λ 5 c λ 10 c λ 15 c λ 20 c (b) EAC λ 50 c λ 75 c φ (NMI) λ 30 c 1 0.8 0.6 0.4 0.2 0 λ 40 c λ 50 c λ 75 c φ (NMI) 1 0.8 0.6 0.4 0.2 0 KMSAD rb−cos−i2 E λc λ 2 c λ 5 c λ 10 c λ 15 c λ 20 c λ 30 c (f) KMSAD λ 40 c HGPA rb−cos−i2 E λc λ 2 c λ 5 c λ 10 c λ 15 c λ 20 c (c) HGPA λ 50 c λ 75 c φ (NMI) λ 30 c 1 0.8 0.6 0.4 0.2 0 λ 40 c λ 50 c λ 75 c φ (NMI) 1 0.8 0.6 0.4 0.2 0 λ 40 c SLSAD rb−cos−i2 E λc λ 2 c λ 5 c λ 10 c λ 15 c λ 20 c λ 30 c (g) SLSAD λ 50 c λ 75 c λ 30 c λ 40 c MCLA rb−cos−i2 E λc λ 2 c λ 5 c λ 10 c λ 15 c λ 20 c (d) MCLA Figure E.16: φ (NMI) boxplots of the self-refined intermodal consensus clustering solutions using the rb-cos-i2 algorithm on the InternetAds data set. direct-cos-i2, graph-cos-i2 and rb-cos-i2 cluster ensembles, as figures E.18, E.19 and E.20 reveal. 368 λ 40 c λ 50 c λ 75 c λ 30 c λ 40 c λ 50 c λ 75 c λ 50 c λ 75 c
φ (NMI) 1 0.8 0.6 0.4 0.2 0 text λ agglo−cos−upgma c E CSPA EAC HGPA MCLA ALSAD KMSAD SLSAD (a) Modality 1 φ (NMI) 0.8 0.6 0.4 0.2 0 Appendix E. Experiments on multimodal consensus clustering image λ agglo−cos−upgma c 1 E CSPA EAC HGPA MCLA ALSAD KMSAD SLSAD (b) Modality 2 φ (NMI) text+image λ agglo−cos−upgma c 1 0.8 0.6 0.4 0.2 0 E CSPA EAC HGPA MCLA ALSAD KMSAD SLSAD (c) Multimodal φ (NMI) 1 0.8 0.6 0.4 0.2 0 λ c agglo−cos−upgma E CSPA EAC HGPA MCLA ALSAD KMSAD SLSAD (d) Intermodal Figure E.17: φ (NMI) boxplots of the unimodal, multimodal and intermodal consensus clustering solutions using the agglo-cos-upgma algorithm on the Corel data set. φ (NMI) 1 0.8 0.6 0.4 0.2 0 text λ direct−cos−i2 c E CSPA EAC HGPA MCLA ALSAD KMSAD SLSAD (a) Modality 1 φ (NMI) 1 0.8 0.6 0.4 0.2 0 image λ direct−cos−i2 c E CSPA EAC HGPA MCLA ALSAD KMSAD SLSAD (b) Modality 2 φ (NMI) 1 0.8 0.6 0.4 0.2 0 text+image λ direct−cos−i2 c E CSPA EAC HGPA MCLA ALSAD KMSAD SLSAD (c) Multimodal φ (NMI) 1 0.8 0.6 0.4 0.2 0 λ c direct−cos−i2 E CSPA EAC HGPA MCLA ALSAD KMSAD SLSAD (d) Intermodal Figure E.18: φ (NMI) boxplots of the unimodal, multimodal and intermodal consensus clustering solutions using the direct-cos-i2 algorithm on the Corel data set. φ (NMI) 1 0.8 0.6 0.4 0.2 0 text λ graph−cos−i2 c E CSPA EAC HGPA MCLA ALSAD KMSAD SLSAD (a) Modality 1 φ (NMI) 1 0.8 0.6 0.4 0.2 0 image λ graph−cos−i2 c E CSPA EAC HGPA MCLA ALSAD KMSAD SLSAD (b) Modality 2 φ (NMI) 1 0.8 0.6 0.4 0.2 0 text+image λ graph−cos−i2 c E CSPA EAC HGPA MCLA ALSAD KMSAD SLSAD (c) Multimodal φ (NMI) 1 0.8 0.6 0.4 0.2 0 λ c graph−cos−i2 E CSPA EAC HGPA MCLA ALSAD KMSAD SLSAD (d) Intermodal Figure E.19: φ (NMI) boxplots of the unimodal, multimodal and intermodal consensus clustering solutions using the graph-cos-i2 algorithm on the Corel data set. E.3.2 Self-refined consensus clustering across modalities In the following paragraphs, the results of applying the consensus self-refining procedure on the intermodal consensus clustering λc are qualitatively described. For starters, the φ (NMI) values of the non-refined and self-refined consensus clusterings obtained on the agglo-cos-upgma are presented in figure E.21. We can observe that, in all cases, there exists at least one refined consensus clustering that attains a higher φ (NMI) value than the non-refined clustering λc. Moreover, in this case, the supraconsensus function is pretty successful in selecting the top quality consensus clustering as the final partition 369
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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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3.3. 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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Page 396 and 397: E.1. CAL500 data set φ (NMI) 1 0.8
Page 398 and 399: E.1. CAL500 data set φ (NMI) CSPA
Page 400 and 401: E.2. InternetAds data set φ (NMI)
Page 402 and 403: E.3. Corel data set φ (NMI) 1 0.8
Page 410 and 411: F.1. Iris data set φ (NMI) 1 0.8 0
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Page 422 and 423: F.11. PenDigits data set φ (NMI) 1
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TESI DOCTORAL - La Salle

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