TESI DOCTORAL - La Salle

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E.1. CAL500 data set φ (NMI) 1 0.8 0.6 0.4 0.2 0 audio λ agglo−cos−upgma c E CSPA EAC HGPA MCLA ALSAD KMSAD SLSAD (a) Modality 1 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 (b) Modality 2 φ (NMI) audio+text 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.1: φ (NMI) boxplots of the unimodal, multimodal and intermodal consensus clustering solutions using the agglo-cos-upgma algorithm on the CAL500 data set. E.1.1 Consensus clustering per modality and across modalities For starters, the quality of the consensus clustering solutions obtained on i) the two original modalities, ii) the fused audio+text multimodal modality, and iii) across the previous three modalities are evaluated. In figure E.1, the results obtained after the application of the proposed multimodal consensus architecture on the cluster ensemble resulting from the compilation of the partitions output by the agglo-cos-upgma clustering algorithm are presented. It can be observed that the quality of the clusterings corresponding to the audio modality are notably better than those obtained on the text mode (except when the EAC consensus function is employed). The early fusion of the auditory and textual features does not introduce any beneficial effect, rather the contrary. The quality of the intermodal consensus clusterings λc corresponding to the combination of three modalities are approximately a trade-off between them. Figures E.2, E.3 and E.4 depict, respectively, the results obtained on the cluster ensembles created upon the direct-cos-i2, graph-cos-i2 and rb-cos-i2 CLUTO clustering algorithms. It can be observed that pretty similar results to the ones just reported are obtained in all cases: that is, the consensus clusterings based on the audio mode attain higher qualities than on the remaining modalities, while multimodal and intermodal consensus clustering solutions are a kind of trade-off between modalities. 360
φ (NMI) 1 0.8 0.6 0.4 0.2 0 audio 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 Appendix E. Experiments on multimodal consensus clustering text 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 audio+text 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.2: φ (NMI) boxplots of the unimodal, multimodal and intermodal consensus clustering solutions using the direct-cos-i2 algorithm on the CAL500 data set. φ (NMI) 1 0.8 0.6 0.4 0.2 0 audio 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 text 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 audio+text 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.3: φ (NMI) boxplots of the unimodal, multimodal and intermodal consensus clustering solutions using the graph-cos-i2 algorithm on the CAL500 data set. φ (NMI) 1 0.8 0.6 0.4 0.2 0 audio 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 text 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 audio+text 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.4: φ (NMI) boxplots of the unimodal, multimodal and intermodal consensus clustering solutions using the rb-cos-i2 algorithm on the CAL500 data set. 361
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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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