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C.4. Computationally optimal RHCA, DHCA and flat consensus comparison CPU time (sec.) CPU time (sec.) CPU time (sec.) CPU time (sec.) 1.1 1 0.9 0.8 0.7 0.6 0.5 20 15 10 5 0 70 60 50 40 30 20 10 0 160 140 120 100 80 60 40 20 0 CSPA RHCA DHCA flat CSPA RHCA DHCA flat CSPA RHCA DHCA flat CSPA RHCA DHCA flat CPU time (sec.) CPU time (sec.) CPU time (sec.) CPU time (sec.) 10 8 6 4 2 12 10 8 6 4 2 14 12 10 8 6 4 16 14 12 10 8 6 4 EAC RHCA DHCA flat EAC RHCA DHCA flat EAC CPU time (sec.) 0.3 0.25 0.2 0.15 0.1 HGPA RHCA DHCA flat CPU time (sec.) 0.35 0.3 0.25 0.2 0.15 0.1 MCLA RHCA DHCA flat CPU time (sec.) 12 10 8 6 4 2 0 ALSAD RHCA DHCA flat (a) Parallel implementation running time, |dfA| =1 CPU time (sec.) 20 15 10 5 0 HGPA RHCA DHCA flat CPU time (sec.) 0.4 0.35 0.3 0.25 0.2 0.15 0.1 MCLA RHCA DHCA flat CPU time (sec.) 20 15 10 5 ALSAD RHCA DHCA flat (b) Parallel implementation running time, |dfA| =10 RHCA DHCA flat CPU time (sec.) 80 70 60 50 40 30 20 10 0 HGPA RHCA DHCA flat CPU time (sec.) 0.5 0.45 0.4 0.35 0.3 0.25 0.2 0.15 MCLA RHCA DHCA flat CPU time (sec.) 70 60 50 40 30 20 10 0 ALSAD RHCA DHCA flat (c) Parallel implementation running time, |dfA| =19 EAC RHCA DHCA flat CPU time (sec.) 160 140 120 100 80 60 40 20 0 HGPA RHCA DHCA flat CPU time (sec.) 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 MCLA RHCA DHCA flat CPU time (sec.) 160 140 120 100 80 60 40 20 0 ALSAD RHCA DHCA flat CPU time (sec.) CPU time (sec.) CPU time (sec.) 1.2 1 0.8 0.6 0.4 0.2 20 15 10 5 0 70 60 50 40 30 20 10 0 160 140 120 100 80 60 40 20 0 (d) Parallel implementation running time, |dfA| =28 CPU time (sec.) KMSAD RHCA DHCA flat KMSAD RHCA DHCA flat KMSAD RHCA DHCA flat KMSAD RHCA DHCA flat CPU time (sec.) CPU time (sec.) CPU time (sec.) CPU time (sec.) 10 8 6 4 2 0 20 15 10 5 0 70 60 50 40 30 20 10 0 160 140 120 100 80 60 40 20 0 SLSAD RHCA DHCA flat SLSAD RHCA DHCA flat SLSAD RHCA DHCA flat SLSAD RHCA DHCA flat Figure C.39: Running times of the computationally optimal parallel RHCA, DHCA and flat consensus architectures on the Ionosphere data collection in the four diversity scenarios |dfA| = {1, 10, 19, 28}. 304
φ (NMI) φ (NMI) φ (NMI) φ (NMI) 1 0.8 0.6 0.4 0.2 0 1 0.8 0.6 0.4 0.2 0 1 0.8 0.6 0.4 0.2 0 1 0.8 0.6 0.4 0.2 0 CSPA E RHCA DHCA flat CSPA E RHCA DHCA flat CSPA E RHCA DHCA flat CSPA E RHCA DHCA flat φ (NMI) φ (NMI) φ (NMI) φ (NMI) 1 0.8 0.6 0.4 0.2 0 1 0.8 0.6 0.4 0.2 0 1 0.8 0.6 0.4 0.2 0 1 0.8 0.6 0.4 0.2 0 EAC Appendix C. Experiments on hierarchical consensus architectures E RHCA DHCA flat EAC E RHCA DHCA flat EAC E RHCA DHCA flat EAC E RHCA DHCA flat φ (NMI) 1 0.8 0.6 0.4 0.2 0 HGPA E RHCA DHCA flat φ (NMI) 1 0.8 0.6 0.4 0.2 0 MCLA E RHCA DHCA flat φ (NMI) 1 0.8 0.6 0.4 0.2 0 ALSAD E RHCA DHCA flat (a) φ (NMI) of the consensus solutions, |dfA| =1 φ (NMI) 1 0.8 0.6 0.4 0.2 0 HGPA E RHCA DHCA flat φ (NMI) 1 0.8 0.6 0.4 0.2 0 MCLA E RHCA DHCA flat φ (NMI) 1 0.8 0.6 0.4 0.2 0 ALSAD E RHCA DHCA flat (b) φ (NMI) of the consensus solutions, |dfA| =10 φ (NMI) 1 0.8 0.6 0.4 0.2 0 HGPA E RHCA DHCA flat φ (NMI) 1 0.8 0.6 0.4 0.2 0 MCLA E RHCA DHCA flat φ (NMI) 1 0.8 0.6 0.4 0.2 0 ALSAD E RHCA DHCA flat (c) φ (NMI) of the consensus solutions, |dfA| =19 φ (NMI) 1 0.8 0.6 0.4 0.2 0 HGPA E RHCA DHCA flat φ (NMI) 1 0.8 0.6 0.4 0.2 0 MCLA E RHCA DHCA flat φ (NMI) 1 0.8 0.6 0.4 0.2 0 ALSAD E RHCA DHCA flat (d) φ (NMI) of the consensus solutions, |dfA| =28 φ (NMI) φ (NMI) φ (NMI) φ (NMI) 1 0.8 0.6 0.4 0.2 0 1 0.8 0.6 0.4 0.2 0 1 0.8 0.6 0.4 0.2 0 1 0.8 0.6 0.4 0.2 0 KMSAD E RHCA DHCA flat KMSAD E RHCA DHCA flat KMSAD E RHCA DHCA flat KMSAD E RHCA DHCA flat φ (NMI) φ (NMI) φ (NMI) φ (NMI) 1 0.8 0.6 0.4 0.2 0 1 0.8 0.6 0.4 0.2 0 1 0.8 0.6 0.4 0.2 0 1 0.8 0.6 0.4 0.2 0 SLSAD E RHCA DHCA flat SLSAD E RHCA DHCA flat SLSAD E RHCA DHCA flat SLSAD E RHCA DHCA flat Figure C.40: φ (NMI) of the consensus solutions yielded by the computationally optimal RHCA, DHCA and flat consensus architectures on the Ionosphere data collection in the four diversity scenarios |dfA| = {1, 10, 19, 28}. 305
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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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D.2. Experiments on selection-based
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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.7. MFeat data set φ (NMI) 1 0.8
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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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