TESI DOCTORAL - La Salle

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C.2. Estimation of the computationally optimal RHCA SERT RHCA (sec.) SERT RHCA (sec.) SERT RHCA (sec.) SERT RHCA (sec.) 3 10 2 2 1 0 s : number of stages 10 −1 2 3 4 9 b : mini−ensemble size (a) Estimated RT, |dfA| =1 6 4 3 3 2 2 1 10 1 s : number of stages 10 0 10 −1 10 1 10 0 10 −1 10 1 10 0 2 3 4 7 8 45 90 b : mini−ensemble size (c) Estimated RT, |dfA| =10 s : number of stages 7 5 4 3 3 2 2 1 2 3 4 5 10 11 85 171 b : mini−ensemble size (e) Estimated RT, |dfA| =19 s : number of stages 7 5 4 4 3 3 2 2 1 2 3 4 5 6 12 13 126 252 b : mini−ensemble size (g) Estimated RT, |dfA| =28 CSPA EAC HGPA MCLA ALSAD KMSAD SLSAD CSPA EAC HGPA MCLA ALSAD KMSAD SLSAD CSPA EAC HGPA MCLA ALSAD KMSAD SLSAD CSPA EAC HGPA MCLA ALSAD KMSAD SLSAD SRT RHCA (sec.) SRT RHCA (sec.) SRT RHCA (sec.) SRT RHCA (sec.) 3 10 2 2 1 0 s : number of stages 10 −1 2 3 4 9 b : mini−ensemble size (b) Real RT, |dfA| =1 6 4 3 3 2 2 1 10 1 s : number of stages 10 0 10 −1 10 1 10 0 10 −1 10 1 10 0 2 3 4 7 8 45 90 b : mini−ensemble size (d) Real RT, |dfA| =10 s : number of stages 7 5 4 3 3 2 2 1 2 3 4 5 10 11 85 171 b : mini−ensemble size (f) Real RT, |dfA| =19 s : number of stages 7 5 4 4 3 3 2 2 1 2 3 4 5 6 12 13 126 252 b : mini−ensemble size (h) Real RT, |dfA| =28 Figure C.1: Estimated and real running times (RT) of the serial RHCA implementation on the Iris data collection in the four diversity scenarios |dfA| = {1, 10, 19, 28}. 254 CSPA EAC HGPA MCLA ALSAD KMSAD SLSAD CSPA EAC HGPA MCLA ALSAD KMSAD SLSAD CSPA EAC HGPA MCLA ALSAD KMSAD SLSAD CSPA EAC HGPA MCLA ALSAD KMSAD SLSAD
PERT RHCA (sec.) PERT RHCA (sec.) PERT RHCA (sec.) PERT RHCA (sec.) 10 −1 10 0 10 −1 10 0 10 −1 10 0 10 −1 Appendix C. Experiments on hierarchical consensus architectures s : number of stages 3 2 2 1 2 3 4 9 b : mini−ensemble size (a) Estimated RT, |dfA| =1 s : number of stages 6 4 3 3 2 2 1 2 3 4 7 8 45 90 b : mini−ensemble size (c) Estimated RT, |dfA| =10 s : number of stages 7 5 4 3 3 2 2 1 2 3 4 5 10 11 85 171 b : mini−ensemble size (e) Estimated RT, |dfA| =19 s : number of stages 7 5 4 4 3 3 2 2 1 2 3 4 5 6 12 13 126 252 b : mini−ensemble size (g) Estimated RT, |dfA| =28 CSPA EAC HGPA MCLA ALSAD KMSAD SLSAD CSPA EAC HGPA MCLA ALSAD KMSAD SLSAD CSPA EAC HGPA MCLA ALSAD KMSAD SLSAD CSPA EAC HGPA MCLA ALSAD KMSAD SLSAD PRT RHCA (sec.) PRT RHCA (sec.) PRT RHCA (sec.) PRT RHCA (sec.) 10 −1 10 0 10 −1 10 0 10 −1 10 0 10 −1 s : number of stages 3 2 2 1 2 3 4 9 b : mini−ensemble size (b) Real RT, |dfA| =1 s : number of stages 6 4 3 3 2 2 1 2 3 4 7 8 45 90 b : mini−ensemble size (d) Real RT, |dfA| =10 s : number of stages 7 5 4 3 3 2 2 1 2 3 4 5 10 11 85 171 b : mini−ensemble size (f) Real RT, |dfA| =19 s : number of stages 7 5 4 4 3 3 2 2 1 2 3 4 5 6 12 13 126 252 b : mini−ensemble size (h) Real RT, |dfA| =28 Figure C.2: Estimated and real running times (RT) of the parallel RHCA implementation on the Iris data collection in the four diversity scenarios |dfA| = {1, 10, 19, 28}. 255 CSPA EAC HGPA MCLA ALSAD KMSAD SLSAD CSPA EAC HGPA MCLA ALSAD KMSAD SLSAD CSPA EAC HGPA MCLA ALSAD KMSAD SLSAD CSPA EAC HGPA MCLA ALSAD KMSAD SLSAD
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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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Page 247 and 248: References Li, S.Z. and G. GuoDong.
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Page 253 and 254: Appendix A Experimental setup A.1 T
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Page 257 and 258: A.2.1 Unimodal data sets Appendix A
Page 259 and 260: A.2.2 Multimodal data sets Appendix
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C.4. Computationally optimal RHCA,
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D.1. Experiments on consensus-based
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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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