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E.3. Corel data set φ (NMI) 1 0.8 0.6 0.4 0.2 0 object 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 collateral λ graph−cos−i2 c E CSPA EAC HGPA MCLA ALSAD KMSAD SLSAD (b) Modality 2 φ (NMI) object+collateral λ graph−cos−i2 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 graph−cos−i2 E CSPA EAC HGPA MCLA ALSAD KMSAD SLSAD (d) Intermodal Figure E.11: φ (NMI) boxplots of the unimodal, multimodal and 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 object 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 collateral 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 object+collateral 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.12: φ (NMI) boxplots of the unimodal, multimodal and intermodal consensus clustering solutions using the rb-cos-i2 algorithm on the InternetAds data set. E.3 Corel data set This section is devoted to the presentation of the results of the multimodal consensus clustering experiments executed on the Corel data collection. On this data set, modalities are image and text features. E.3.1 Consensus clustering per modality and across modalities For starters, figure E.17 depicts the unimodal, multimodal and intermodal consensus clusterings obtained on the agglo-cos-upgma cluster ensemble. Notice the notable differences between both modalities, as clustering this collection using the textual features of the objects leads to the obtention of better partitions than those obtained on the image modality. Apparently, the multimodal modality resulting from the early fusion of textual and visual features, yields clusterings the quality of which is equal or slightly lower than the textual ones. Thus, in this case, multimodality brings about no gains as regards the obtention of higher quality partitions. <strong>La</strong>st, the intermodal consensus clustering λc attains φ (NMI) values 366
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 Appendix E. Experiments on multimodal consensus clustering 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.13: φ (NMI) boxplots of the self-refined intermodal consensus clustering solutions using the agglo-cos-upgma algorithm on the InternetAds data set. 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 λ 30 c (a) CSPA φ (NMI) 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 λ 30 c λ 40 c (e) ALSAD EAC direct−cos−i2 E λc λ 2 c λ 5 c λ 50 c λ 75 c λ 10 c λ 15 c λ 20 c λ 30 c (b) EAC φ (NMI) 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 λ 40 c (f) KMSAD HGPA direct−cos−i2 E λc λ 2 c λ 5 c λ 10 c λ 15 c λ 20 c λ 30 c (c) HGPA λ 50 c λ 75 c φ (NMI) 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 direct−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 direct−cos−i2 E λc λ 2 c λ 5 c λ 10 c λ 15 c λ 20 c λ 30 c (d) MCLA Figure E.14: φ (NMI) boxplots of the self-refined intermodal consensus clustering solutions using the direct-cos-i2 algorithm on the InternetAds data set. comparable to the best text modality based partitions when the CSPA, ALSAD and KM- SAD consensus functions are employed, while it constitutes a trade-off between modalities when the remaining clustering combiners are used. A pretty similar performance is observed when the consensus process is applied on the 367 λ 40 c λ 50 c λ 75 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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TESI DOCTORAL - La Salle

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