TESI DOCTORAL - La Salle

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List of Figures C.35 Running times of the computationally optimal serial RHCA, DHCA and flat consensus architectures on the Glass data collection in the four diversity scenarios |dfA| = {1, 10, 19, 28} . . . . . . . . . . . . . . . . . . . . . . . . . 299 C.36 Running times of the computationally optimal parallel RHCA, DHCA and flat consensus architectures on the Glass data collection in the four diversity scenarios |dfA| = {1, 10, 19, 28} . . . . . . . . . . . . . . . . . . . . . . . . . 300 C.37 φ (NMI) of the consensus solutions yielded by the computationally optimal RHCA, DHCA and flat consensus architectures on the Glass data collection in the four diversity scenarios |dfA| = {1, 10, 19, 28} . . . . . . . . . . . . . . 302 C.38 Running times of the computationally optimal serial RHCA, DHCA and flat consensus architectures on the Ionosphere data collection in the four diversity scenarios |dfA| = {1, 10, 19, 28} . . . . . . . . . . . . . . . . . . . . . . . . . 303 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 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 C.41 Running times of the computationally optimal serial RHCA, DHCA and flat consensus architectures on the WDBC data collection in the four diversity scenarios |dfA| = {1, 10, 19, 28} . . . . . . . . . . . . . . . . . . . . . . . . . 307 C.42 Running times of the computationally optimal parallel RHCA, DHCA and flat consensus architectures on the WDBC data collection in the four diversity scenarios |dfA| = {1, 10, 19, 28} . . . . . . . . . . . . . . . . . . . . . . . . . 308 C.43 φ (NMI) of the consensus solutions yielded by the computationally optimal RHCA, DHCA and flat consensus architectures on the WDBC data collection in the four diversity scenarios |dfA| = {1, 10, 19, 28} . . . . . . . . . . . . . . 309 C.44 Running times of the computationally optimal serial RHCA, DHCA and flat consensus architectures on the Balance data collection in the four diversity scenarios |dfA| = {1, 10, 19, 28} . . . . . . . . . . . . . . . . . . . . . . . . . 311 C.45 Running times of the computationally optimal parallel RHCA, DHCA and flat consensus architectures on the Balance data collection in the four diversity scenarios |dfA| = {1, 10, 19, 28} . . . . . . . . . . . . . . . . . . . . . . . 312 C.46 φ (NMI) of the consensus solutions yielded by the computationally optimal RHCA, DHCA and flat consensus architectures on the Balance data collection in the four diversity scenarios |dfA| = {1, 10, 19, 28} . . . . . . . . . . . 313 C.47 Running times of the computationally optimal serial RHCA, DHCA and flat consensus architectures on the MFeat data collection in the four diversity scenarios |dfA| = {1, 10, 19, 28} . . . . . . . . . . . . . . . . . . . . . . . . . 315 C.48 Running times of the computationally optimal parallel RHCA, DHCA and flat consensus architectures on the MFeat data collection in the four diversity scenarios |dfA| = {1, 10, 19, 28} . . . . . . . . . . . . . . . . . . . . . . . . . 316 xxviii
List of Figures C.49 φ (NMI) of the consensus solutions yielded by the computationally optimal RHCA, DHCA and flat consensus architectures on the MFeat data collection in the four diversity scenarios |dfA| = {1, 10, 19, 28} . . . . . . . . . . . . . . 317 C.50 Running times of the computationally optimal serial RHCA, DHCA and flat consensus architectures on the miniNG data collection in the four diversity scenarios |dfA| = {1, 10, 19, 28} . . . . . . . . . . . . . . . . . . . . . . . . . 319 C.51 Running times of the computationally optimal parallel RHCA, DHCA and flat consensus architectures on the miniNG data collection in the four diversity scenarios |dfA| = {1, 10, 19, 28} . . . . . . . . . . . . . . . . . . . . . . . 320 C.52 φ (NMI) of the consensus solutions yielded by the computationally optimal RHCA, DHCA and flat consensus architectures on the miniNG data collection in the four diversity scenarios |dfA| = {1, 10, 19, 28} . . . . . . . . . . . 321 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 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 C.55 φ (NMI) of the consensus solutions yielded by the computationally optimal RHCA, DHCA and flat consensus architectures on the Segmentation data collection in the four diversity scenarios |dfA| = {1, 10, 19, 28} . . . . . . . . 325 C.56 Running times of the computationally optimal serial RHCA, DHCA and flat consensus architectures on the BBC data collection in the four diversity scenarios |dfA| = {1, 10, 19, 28} . . . . . . . . . . . . . . . . . . . . . . . . . 326 C.57 Running times of the computationally optimal parallel RHCA, DHCA and flat consensus architectures on the BBC data collection in the four diversity scenarios |dfA| = {1, 10, 19, 28} . . . . . . . . . . . . . . . . . . . . . . . . . 327 C.58 φ (NMI) of the consensus solutions yielded by the computationally optimal RHCA, DHCA and flat consensus architectures on the BBC data collection in the four diversity scenarios |dfA| = {1, 10, 19, 28} . . . . . . . . . . . . . . 328 C.59 Running times of the computationally optimal serial RHCA, DHCA and flat consensus architectures on the PenDigits data collection in the four diversity scenarios |dfA| = {1, 10, 19, 28} . . . . . . . . . . . . . . . . . . . . . . . . . 330 C.60 Running times of the computationally optimal parallel RHCA, DHCA and flat consensus architectures on the PenDigits data collection in the four diversity scenarios |dfA| = {1, 10, 19, 28} . . . . . . . . . . . . . . . . . . . . . 331 C.61 φ (NMI) of the consensus solutions yielded by the computationally optimal RHCA, DHCA and flat consensus architectures on the PenDigits data collection in the four diversity scenarios |dfA| = {1, 10, 19, 28} . . . . . . . . . . 332 D.1 φ (NMI) boxplots of the self-refined consensus clustering solutions on the Iris data collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335 xxix
Page 1: C.I.F. G: 59069740 Universitat Ramo
Page 5: Resum En segmentar de forma no supe
Page 9: Abstract When facing the task of pa
Page 12 and 13: Contents 2.2.6 Consensus functions
Page 14 and 15: Contents A.5 Consensus functions .
Page 16 and 17: Contents D.2.5 WDBC data set . . .
Page 18 and 19: List of Tables 3.13 Relative percen
Page 20 and 21: List of Tables 5.15 Relative φ (NM
Page 23 and 24: List of Figures 1.1 Evolution of th
Page 25 and 26: List of Figures 4.2 Decreasingly or
Page 27: List of Figures C.18 Estimated and
Page 31 and 32: List of Figures D.22 φ (NMI) boxpl
Page 33: List of Algorithms 6.1 Symbolic des
Page 36 and 37: List of symbols OΛ: object co-asso
Page 38 and 39: Chapter 1. Framework of the thesis
Page 40 and 41: 1.1. Knowledge discovery and data m
Page 42 and 43: 1.1. Knowledge discovery and data m
Page 44 and 45: 1.2. Clustering in knowledge discov
Page 54 and 55: 1.3. Multimodal clustering in clust
Page 56 and 57: 1.4. Clustering indeterminacies exe
Page 58 and 59: 1.4. Clustering indeterminacies The
Page 60 and 61: 1.4. Clustering indeterminacies Dat
Page 62 and 63: 1.5. Motivation and contributions o
Page 64 and 65: Chapter 2. Cluster ensembles and co
Page 66 and 67: fuzzy consensus clustering solution
Page 68 and 69: 2.1. Related work on cluster ensemb
Page 70 and 71: 2.2. Related work on consensus func
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2.2. Related work on consensus func
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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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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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