TESI DOCTORAL - La Salle

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F.11. PenDigits data set φ (NMI) 1 0.8 0.6 0.4 0.2 0 10 0 PENDIGITS 10 1 CPU time (sec.) 10 2 HGPA MCLA VMA BC CC PC SC Figure F.11: φ (NMI) vs CPU time mean ± 2-standard deviation regions of the soft consensus functions on the PenDigits data collection. HGPA MCLA VMA BC CC PC SC HGPA ——— × 0.0001 0.0005 0.0001 × × MCLA 0.0001 ——— 0.0015 0.0001 0.0001 0.0273 0.0269 VMA 0.0001 0.0001 ——— 0.0001 0.0001 0.0002 0.0002 BC 0.0001 0.0001 0.0001 ——— 0.0001 0.0097 0.0098 CC 0.0001 0.0001 0.0001 × ——— 0.0001 0.0001 PC 0.0001 0.0001 × 0.0001 0.0001 ——— × SC 0.0001 0.0001 × 0.0001 0.0001 × ——— Table F.11: Significance levels p corresponding to the pairwise comparison of soft consensus functions using a t-paired test on the PenDigits data set. The upper and lower triangular sections of the table correspond to the comparison in terms of CPU time and φ (NMI) , respectively. Statistically non-significant differences (p >0.05) are denoted by the symbol ×. 386
C.I.F. G: 59069740 Universitat Ramon Lull Fundació Privada. Rgtre. Fund. Generalitat de Catalunya núm. 472 (28-02-90) Aquesta Tesi Doctoral ha estat defensada el dia ____ d __________________ de ____ al Centre _______________________________________________________________ de la Universitat Ramon Llull davant el Tribunal format pels Doctors sotasignants, havent obtingut la qualificació: President/a _______________________________ Vocal _______________________________ Vocal _______________________________ Vocal _______________________________ Secretari/ària _______________________________ Doctorand/a C. Claravall, 1-3 08022 Barcelona Tel. 936 022 200 Fax 936 022 249 E-mail: urlsc@sec.url.es www.url.es
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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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C.4. Computationally optimal RHCA,
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D.1. Experiments on consensus-based
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Page 396 and 397: E.1. CAL500 data set φ (NMI) 1 0.8
Page 398 and 399: E.1. CAL500 data set φ (NMI) CSPA
Page 400 and 401: E.2. InternetAds data set φ (NMI)
Page 402 and 403: E.3. Corel data set φ (NMI) 1 0.8
Page 410 and 411: F.1. Iris data set φ (NMI) 1 0.8 0
Page 412 and 413: F.3. Glass data set CSPA EAC HGPA M
Page 414 and 415: F.5. WDBC data set φ (NMI) 1 0.8 0
Page 416 and 417: F.7. MFeat data set φ (NMI) 1 0.8
Page 418 and 419: F.9. Segmentation data set φ (NMI)
Page 420 and 421: F.10. BBC data set φ (NMI) 1 0.8 0
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