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E.3. Corel data set φ (NMI) 1 0.8 0.6 0.4 0.2 0 text λ rb−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 rb−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 rb−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 rb−cos−i2 E CSPA EAC HGPA MCLA ALSAD KMSAD SLSAD (d) Intermodal Figure E.20: φ (NMI) boxplots of the unimodal, multimodal and intermodal consensus clustering solutions using the rb-cos-i2 algorithm on the Corel data set. λ final c , which is again highlighted by means of a vertical green dashed line. The performance of the self-refining procedure is equally satisfying when conducted on the cluster ensembles created by means of the three remaining clustering algorithms, as figures E.22, E.23 and E.24 depict. However, the selection accuracy of the supraconsensus function is somewhat inconsistent, as already observed on the previous data sets. φ (NMI) CSPA agglo−cos−upgma 1 0.8 0.6 0.4 0.2 0 E λc λ 2 c λ 5 c λ 10 c λ 15 c λ 20 c (a) CSPA φ (NMI) λ 30 c λ 40 c λ 50 c λ 75 c φ (NMI) 1 0.8 0.6 0.4 0.2 0 EAC agglo−cos−upgma E λc λ 2 c λ 5 c ALSAD agglo−cos−upgma 1 0.8 0.6 0.4 0.2 0 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 λ 10 c λ 15 c λ 20 c (b) EAC λ 50 c λ 75 c φ (NMI) λ 30 c λ 40 c λ 50 c λ 75 c φ (NMI) HGPA agglo−cos−upgma 1 0.8 0.6 0.4 0.2 0 E λc λ 2 c λ 5 c KMSAD agglo−cos−upgma 1 0.8 0.6 0.4 0.2 0 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 λ 10 c λ 15 c λ 20 c (c) HGPA λ 50 c λ 75 c φ (NMI) λ 30 c λ 40 c λ 50 c λ 75 c φ (NMI) MCLA agglo−cos−upgma 1 0.8 0.6 0.4 0.2 0 E λc λ 2 c λ 5 c SLSAD agglo−cos−upgma 1 0.8 0.6 0.4 0.2 0 E λc λ 2 c λ 5 c λ 10 c λ 15 c λ 20 c λ 30 c (g) SLSAD λ 10 c λ 15 c λ 20 c (d) MCLA Figure E.21: φ (NMI) boxplots of the self-refined intermodal consensus clustering solutions using the agglo-cos-upgma algorithm on the Corel data set. 370 λ 40 c λ 50 c λ 75 c λ 30 c λ 40 c λ 50 c λ 75 c
φ (NMI) 1 0.8 0.6 0.4 0.2 0 CSPA direct−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 direct−cos−i2 E λc λ 2 c λ 5 c λ 10 c λ 15 c λ 20 c 0 Appendix E. Experiments on multimodal consensus clustering λ 30 c (e) ALSAD λ 40 c EAC direct−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 direct−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 direct−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 direct−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 direct−cos−i2 E λc λ 2 c λ 5 c λ 10 c λ 15 c λ 20 c (d) MCLA Figure E.22: φ (NMI) boxplots of the self-refined 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 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 0 λ 40 c SLSAD graph−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 graph−cos−i2 E λc λ 2 c λ 5 c λ 10 c λ 15 c λ 20 c (d) MCLA Figure E.23: φ (NMI) boxplots of the self-refined intermodal consensus clustering solutions using the graph-cos-i2 algorithm on the Corel data set. 371 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
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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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3.4. Flat vs. hierarchical consensu
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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
Page 412 and 413: F.3. Glass data set CSPA EAC HGPA M
Page 414 and 415: F.5. WDBC data set φ (NMI) 1 0.8 0
Page 416 and 417: F.7. MFeat data set φ (NMI) 1 0.8
Page 418 and 419: F.9. Segmentation data set φ (NMI)
Page 420 and 421: F.10. BBC data set φ (NMI) 1 0.8 0
Page 422 and 423: F.11. PenDigits data set φ (NMI) 1
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TESI DOCTORAL - La Salle

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