TESI DOCTORAL - La Salle

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C.3. Estimation of the computationally optimal DHCA SERT DHCA (sec.) SERT DHCA (sec.) SERT DHCA (sec.) SERT DHCA (sec.) 10 0 |df A | = 1 , |df D | = 1 , |df R | = 6 ADR ARD DAR DRA RAD RDA flat DHCA variant (a) Estimated RT, |dfA| =1 |df A | = 10 , |df D | = 1 , |df R | = 6 10 ADR ARD DAR DRA RAD RDA flat 0 DHCA variant 10 4 10 2 (c) Estimated RT, |dfA| =10 ADR ARD DAR DRA RAD RDA flat DHCA variant 10 4 10 2 |df A | = 19 , |df D | = 1 , |df R | = 6 (e) Estimated RT, |dfA| =19 |df A | = 28 , |df D | = 1 , |df R | = 6 ADR ARD DAR DRA RAD RDA flat DHCA variant (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 DHCA (sec.) SRT DHCA (sec.) SRT DHCA (sec.) SRT DHCA (sec.) 10 0 |df A | = 1 , |df D | = 1 , |df R | = 6 ADR ARD DAR DRA RAD RDA flat DHCA variant (b) Real RT, |dfA| =1 |df A | = 10 , |df D | = 1 , |df R | = 6 10 ADR ARD DAR DRA RAD RDA flat 0 DHCA variant 10 4 10 2 (d) Real RT, |dfA| =10 |df A | = 19 , |df D | = 1 , |df R | = 6 ADR ARD DAR DRA RAD RDA flat DHCA variant 10 4 10 2 (f) Real RT, |dfA| =19 |df A | = 28 , |df D | = 1 , |df R | = 6 ADR ARD DAR DRA RAD RDA flat DHCA variant (h) Real 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 Figure C.27: Estimated and real running times (RT) of the serial DHCA implementation on the Mfeat data collection in the four diversity scenarios |dfA| = {1, 10, 19, 28}. 288
PERT DHCA (sec.) PERT DHCA (sec.) PERT DHCA (sec.) PERT DHCA (sec.) 10 2 10 0 Appendix C. Experiments on hierarchical consensus architectures |df A | = 1 , |df D | = 1 , |df R | = 6 ADR ARD DAR DRA RAD RDA flat DHCA variant 10 2 10 0 (a) Estimated RT, |dfA| =1 ADR ARD DAR DRA RAD RDA flat DHCA variant 10 2 10 0 |df A | = 10 , |df D | = 1 , |df R | = 6 (c) Estimated RT, |dfA| =10 ADR ARD DAR DRA RAD RDA flat DHCA variant 10 2 10 0 |df A | = 19 , |df D | = 1 , |df R | = 6 (e) Estimated RT, |dfA| =19 |df A | = 28 , |df D | = 1 , |df R | = 6 ADR ARD DAR DRA RAD RDA flat DHCA variant (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 DHCA (sec.) PRT DHCA (sec.) PRT DHCA (sec.) PRT DHCA (sec.) 10 2 10 0 |df A | = 1 , |df D | = 1 , |df R | = 6 ADR ARD DAR DRA RAD RDA flat DHCA variant 10 2 10 0 (b) Real RT, |dfA| =1 |df A | = 10 , |df D | = 1 , |df R | = 6 ADR ARD DAR DRA RAD RDA flat DHCA variant 10 2 10 0 (d) Real RT, |dfA| =10 |df A | = 19 , |df D | = 1 , |df R | = 6 ADR ARD DAR DRA RAD RDA flat DHCA variant 10 2 10 0 (f) Real RT, |dfA| =19 |df A | = 28 , |df D | = 1 , |df R | = 6 ADR ARD DAR DRA RAD RDA flat DHCA variant (h) Real 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 Figure C.28: Estimated and real running times (RT) of the parallel DHCA implementation on the Mfeat data collection in the four diversity scenarios |dfA| = {1, 10, 19, 28}. 289
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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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B.1. Clustering indeterminacies in
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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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TESI DOCTORAL - La Salle

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