TESI DOCTORAL - La Salle

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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.) 100 80 60 40 20 350 300 250 200 150 100 700 600 500 400 300 200 900 800 700 600 500 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.) 16000 14000 12000 10000 8000 6000 4000 2000 0 16000 14000 12000 10000 8000 6000 4000 2000 0 12000 10000 8000 6000 4000 2000 0 12000 10000 8000 6000 4000 2000 0 EAC RHCA DHCA flat EAC RHCA DHCA flat EAC RHCA DHCA flat EAC RHCA DHCA flat CPU time (sec.) 1.8 1.6 1.4 1.2 1 0.8 0.6 HGPA RHCA DHCA flat CPU time (sec.) 2.2 2 1.8 1.6 1.4 1.2 1 0.8 0.6 MCLA RHCA DHCA flat CPU time (sec.) x ALSAD 104 4 3.5 3 2.5 2 1.5 1 0.5 0 RHCA DHCA (a) Serial implementation running time, |dfA| =1 CPU time (sec.) 120 100 80 60 40 20 HGPA RHCA DHCA flat CPU time (sec.) 160 140 120 100 80 60 40 20 0 MCLA RHCA DHCA flat CPU time (sec.) 2.5 2 1.5 1 0.5 0 flat x ALSAD 104 3 RHCA DHCA (b) Serial implementation running time, |dfA| =10 CPU time (sec.) 400 300 200 100 0 HGPA RHCA DHCA flat CPU time (sec.) 30 25 20 15 10 MCLA RHCA DHCA flat CPU time (sec.) 8000 6000 4000 2000 0 flat ALSAD RHCA DHCA flat (c) Serial implementation running time, |dfA| =19 CPU time (sec.) 800 600 400 200 0 HGPA RHCA DHCA flat CPU time (sec.) 50 45 40 35 30 25 MCLA RHCA DHCA flat CPU time (sec.) 8000 7000 6000 5000 4000 3000 2000 1000 ALSAD RHCA DHCA flat (d) Serial implementation running time, |dfA| =28 CPU time (sec.) CPU time (sec.) CPU time (sec.) CPU time (sec.) 800 600 400 200 0 2000 1500 1000 500 0 3500 3000 2500 2000 1500 1000 500 3500 3000 2500 2000 1500 1000 500 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.) 14000 12000 10000 8000 6000 4000 2000 0 10000 8000 6000 4000 2000 0 5000 4000 3000 2000 1000 0 8000 7000 6000 5000 4000 3000 2000 1000 SLSAD RHCA DHCA RHCA DHCA flat SLSAD flat SLSAD RHCA DHCA flat SLSAD RHCA DHCA flat Figure C.53: Running times of the computationally optimal serial RHCA, DHCA and flat consensus architectures on the Segmentation data collection in the four diversity scenarios |dfA| = {1, 10, 19, 28}. 322
CPU time (sec.) CPU time (sec.) CPU time (sec.) CPU time (sec.) 30 25 20 15 10 140 120 100 80 60 40 20 400 300 200 100 0 1000 800 600 400 200 CSPA RHCA DHCA 0 flat CSPA RHCA DHCA flat CSPA RHCA DHCA flat CSPA CPU time (sec.) RHCA DHCA flat CPU time (sec.) CPU time (sec.) 4000 3500 3000 2500 2000 1500 1000 500 0 CPU time (sec.) 5000 4000 3000 2000 1000 0 2000 1500 1000 500 2000 1500 1000 500 EAC RHCA DHCA flat EAC Appendix C. Experiments on hierarchical consensus architectures CPU time (sec.) 1.8 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 HGPA RHCA DHCA flat CPU time (sec.) 2 1.5 1 0.5 MCLA RHCA DHCA flat CPU time (sec.) 12000 10000 8000 6000 4000 2000 0 ALSAD RHCA DHCA (a) Parallel implementation running time, |dfA| =1 RHCA DHCA flat CPU time (sec.) 120 100 80 60 40 20 0 HGPA RHCA DHCA flat CPU time (sec.) 160 140 120 100 80 60 40 20 0 MCLA RHCA DHCA flat CPU time (sec.) 6000 5000 4000 3000 2000 1000 0 flat ALSAD RHCA DHCA flat CPU time (sec.) CPU time (sec.) 500 400 300 200 100 0 900 800 700 600 500 400 300 200 100 (b) Parallel implementation running time, |dfA| =10 EAC RHCA DHCA flat CPU time (sec.) 400 300 200 100 0 HGPA RHCA DHCA flat CPU time (sec.) 3 2.5 2 1.5 1 0.5 MCLA RHCA DHCA flat CPU time (sec.) 700 600 500 400 300 200 ALSAD RHCA DHCA flat CPU time (sec.) 1200 1000 (c) Parallel implementation running time, |dfA| =19 EAC RHCA DHCA flat CPU time (sec.) 800 600 400 200 0 HGPA RHCA DHCA flat CPU time (sec.) 5 4.5 4 3.5 3 2.5 2 1.5 1 MCLA RHCA DHCA flat CPU time (sec.) 1000 900 800 700 600 500 400 300 200 ALSAD RHCA DHCA flat 800 600 400 200 1200 1000 (d) Parallel implementation running time, |dfA| =28 CPU time (sec.) 800 600 400 200 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.) 3500 3000 2500 2000 1500 1000 500 3500 3000 2500 2000 1500 1000 500 0 1200 1000 800 600 400 200 0 1200 1100 1000 900 800 700 600 500 400 SLSAD RHCA DHCA flat SLSAD RHCA DHCA flat SLSAD RHCA DHCA flat SLSAD RHCA DHCA flat Figure C.54: Running times of the computationally optimal parallel RHCA, DHCA and flat consensus architectures on the Segmentation data collection in the four diversity scenarios |dfA| = {1, 10, 19, 28}. 323
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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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Page 402 and 403: E.3. Corel data set φ (NMI) 1 0.8
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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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