TESI DOCTORAL - La Salle

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List of Figures B.11 φ (NMI) histograms on the BBC data set . . . . . . . . . . . . . . . . . . . . 240 B.12 φ (NMI) histograms on the PenDigits data set . . . . . . . . . . . . . . . . . . 240 B.13 φ (NMI) histograms on the CAL500 data set . . . . . . . . . . . . . . . . . . 244 B.14 φ (NMI) histograms on the Corel data set . . . . . . . . . . . . . . . . . . . . 245 B.15 φ (NMI) histograms on the InternetAds data set . . . . . . . . . . . . . . . . 246 B.16 φ (NMI) histograms on the IsoLetters data set . . . . . . . . . . . . . . . . . . 247 C.1 Estimated and real running times of the serial RHCA implementation on the Iris data collection in the four diversity scenarios . . . . . . . . . . . . . . . 254 C.2 Estimated and real running times of the parallel RHCA implementation on the Iris data collection in the four diversity scenarios . . . . . . . . . . . . . 255 C.3 Estimated and real running times of the serial RHCA implementation on the Wine data collection in the four diversity scenarios . . . . . . . . . . . . . . 257 C.4 Estimated and real running times of the parallel RHCA implementation on the Wine data collection in the four diversity scenarios . . . . . . . . . . . . 258 C.5 Estimated and real running times of the serial RHCA implementation on the Glass data collection in the four diversity scenarios . . . . . . . . . . . . . . 259 C.6 Estimated and real running times of the parallel RHCA implementation on the Glass data collection in the four diversity scenarios . . . . . . . . . . . . 260 C.7 Estimated and real running times of the serial RHCA implementation on the Ionosphere data collection in the four diversity scenarios . . . . . . . . . . . 262 C.8 Estimated and real running times of the parallel RHCA implementation on the Ionosphere data collection in the four diversity scenarios . . . . . . . . . 263 C.9 Estimated and real running times of the serial RHCA implementation on the WDBC data collection in the four diversity scenarios . . . . . . . . . . . . . 264 C.10 Estimated and real running times of the parallel RHCA implementation on the WDBC data collection in the four diversity scenarios . . . . . . . . . . . 265 C.11 Estimated and real running times of the serial RHCA implementation on the Balance data collection in the four diversity scenarios . . . . . . . . . . . . 267 C.12 Estimated and real running times of the parallel RHCA implementation on the Balance data collection in the four diversity scenarios . . . . . . . . . . 268 C.13 Estimated and real running times of the serial RHCA implementation on the Mfeat data collection in the four diversity scenarios . . . . . . . . . . . . . . 269 C.14 Estimated and real running times of the parallel RHCA implementation on the Mfeat data collection in the four diversity scenarios . . . . . . . . . . . 270 C.15 Estimated and real running times of the serial DHCA implementation on the Iris data collection in the four diversity scenarios . . . . . . . . . . . . . . . 274 C.16 Estimated and real running times of the parallel DHCA implementation on the Iris data collection in the four diversity scenarios . . . . . . . . . . . . . 275 C.17 Estimated and real running times of the serial DHCA implementation on the Wine data collection in the four diversity scenarios . . . . . . . . . . . . . . 276 xxvi
List of Figures C.18 Estimated and real running times of the parallel DHCA implementation on the Wine data collection in the four diversity scenarios . . . . . . . . . . . . 277 C.19 Estimated and real running times of the serial DHCA implementation on the Glass data collection in the four diversity scenarios . . . . . . . . . . . . . . 279 C.20 Estimated and real running times of the parallel DHCA implementation on the Glass data collection in the four diversity scenarios . . . . . . . . . . . . 280 C.21 Estimated and real running times of the serial DHCA implementation on the Ionosphere data collection in the four diversity scenarios . . . . . . . . . . . 281 C.22 Estimated and real running times of the parallel DHCA implementation on the Ionosphere data collection in the four diversity scenarios . . . . . . . . . 282 C.23 Estimated and real running times of the serial DHCA implementation on the WDBC data collection in the four diversity scenarios . . . . . . . . . . . . . 284 C.24 Estimated and real running times of the parallel DHCA implementation on the WDBC data collection in the four diversity scenarios . . . . . . . . . . . 285 C.25 Estimated and real running times of the serial DHCA implementation on the Balance data collection in the four diversity scenarios . . . . . . . . . . . . 286 C.26 Estimated and real running times of the parallel DHCA implementation on the Balance data collection in the four diversity scenarios . . . . . . . . . . 287 C.27 Estimated and real running times of the serial DHCA implementation on the Mfeat data collection in the four diversity scenarios . . . . . . . . . . . . . . 288 C.28 Estimated and real running times of the parallel DHCA implementation on the Mfeat data collection in the four diversity scenarios . . . . . . . . . . . 289 C.29 Running times of the computationally optimal serial RHCA, DHCA and flat consensus architectures on the Iris data collection in the four diversity scenarios |dfA| = {1, 10, 19, 28} . . . . . . . . . . . . . . . . . . . . . . . . . 292 C.30 Running times of the computationally optimal parallel RHCA, DHCA and flat consensus architectures on the Iris data collection in the four diversity scenarios |dfA| = {1, 10, 19, 28} . . . . . . . . . . . . . . . . . . . . . . . . . 293 C.31 φ (NMI) of the consensus solutions yielded by the computationally optimal RHCA, DHCA and flat consensus architectures on the Iris data collection in the four diversity scenarios |dfA| = {1, 10, 19, 28} . . . . . . . . . . . . . . . 294 C.32 Running times of the computationally optimal serial RHCA, DHCA and flat consensus architectures on the Wine data collection in the four diversity scenarios |dfA| = {1, 10, 19, 28} . . . . . . . . . . . . . . . . . . . . . . . . . 296 C.33 Running times of the computationally optimal parallel RHCA, DHCA and flat consensus architectures on the Wine data collection in the four diversity scenarios |dfA| = {1, 10, 19, 28} . . . . . . . . . . . . . . . . . . . . . . . . . 297 C.34 φ (NMI) of the consensus solutions yielded by the computationally optimal RHCA, DHCA and flat consensus architectures on the Wine data collection in the four diversity scenarios |dfA| = {1, 10, 19, 28} . . . . . . . . . . . . . . 298 xxvii
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: List of Figures 4.2 Decreasingly or
Page 29 and 30: List of Figures C.49 φ (NMI) of th
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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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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TESI DOCTORAL - La Salle

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