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5.3. Multimodal consensus clustering results φ (NMI) 1 0.8 0.6 0.4 0.2 0 image λ rb−cos−i2 c E CSPA EAC HGPA MCLA ALSAD KMSAD SLSAD (a) Modality 1 φ (NMI) 1 0.8 0.6 0.4 0.2 0 speech λ rb−cos−i2 c E CSPA EAC HGPA MCLA ALSAD KMSAD SLSAD (b) Modality 2 φ (NMI) 1 0.8 0.6 0.4 0.2 0 image+speech λ rb−cos−i2 c E CSPA EAC HGPA MCLA ALSAD KMSAD SLSAD (c) Multimodal φ (NMI) 1 0.8 0.6 0.4 0.2 0 λ c rb−cos−i2 E CSPA EAC HGPA MCLA ALSAD KMSAD SLSAD (d) Intermodal Figure 5.6: φ (NMI) boxplots of the unimodal, multimodal and intermodal consensus clustering solutions using the rb-cos-i2 algorithm on the IsoLetters data set. clustering, it is necessary to conduct a more quantitative and generic analysis across the experiments conducted on the |dfA| = 28 cluster ensembles created using all the clustering algorithms from the CLUTO toolbox upon the four multimodal data collections employed in this work. Unimodal and multimodal consensus clustering vs their cluster ensembles mod 1 Firstly, we have evaluated the quality of the two unimodal (λc mod 1+mod 2 multimodal (λc have been created upon. mod 2 and λc )andthe ) consensus clusterings with respect to the cluster ensembles they In order to evaluate how the consensus clusterings compare to their associated cluster ensembles, we have computed the percentage of cluster ensemble components that attain ahigherφ (NMI) than the evaluated consensus clustering. Quite obviously, the smaller this percentage, the higher robustness to clustering indeterminacies is achieved. The results of this analysis are presented in table 5.2. Care must be taken in analyzing the presented percentages, due to the fact that the two unimodal and the multimodal consensus clusterings have been created upon different cluster ensembles, which makes comparisons across columns (i.e. across consensus functions) fair, but the same does not hold for comparisons across rows (i.e. across consensus clusterings). If the performance of the seven consensus functions is contrasted, it can be observed that EAC and HGPA yield, in most cases, the worst results, as the consensus clusterings they yield (either unimodal or multimodal) are, in average, worse than the 76.9% and 78.8% of the components of the cluster ensemble they are created upon —a percentage that goes below 30% in the case of the best performing consensus functions (CSPA, ALSAD and KMSAD). These results confirm that there may exist great differences between consensus functions as far as the quality of the consensus clusterings is concerned, so care must be taken when choosing which ones are applied. If averages are taken for summarization purposes, unimodal consensus clusterings are better than the 49.2% of their corresponding cluster ensemble components, while this percentage rises to 56.5% when multimodal consensus clusterings are considered. 144
Chapter 5. Multimedia clustering based on cluster ensembles Data Consensus Consensus function set type CSPA EAC HGPA MCLA ALSAD KMSAD SLSAD λ IsoLetters image c 21.1 60.1 63.4 40.9 20.4 26.6 40.2 λ speech c 43.9 91.5 79.4 47.4 18.3 34.6 77.4 λ image+speech c 17.1 51.8 59.9 34.1 16.7 25.0 39.1 λ CAL500 audio c 16.5 86.3 67.7 29.9 9.9 12.0 70.3 λ text c 29.7 83.5 52.6 59.3 32.6 30.5 75.7 λ audio+text c 21.9 94.9 54.8 48.0 16.9 20.3 61.9 λ InternetAds object c 47.8 62.2 99.1 44.7 39.8 40.5 61.6 λ collateral c 55.3 52.4 99.4 40.9 45.1 37.6 59.6 λ object+collateral c 41.6 64.5 99.7 29.9 29.2 34.6 37.3 λ Corel image c 12.7 93.8 96.2 50.0 19.1 26.7 79.1 λ text c 10.4 89.2 88.1 43.7 28.8 23.7 77.8 λ image+text c 7.5 92.2 85.1 40.2 10.9 20.2 64.1 145 Table 5.2: Percentage of cluster ensemble components that attain a higher φ (NMI) than the unimodal and multimodal consensus clusterings, across the four multimedia data collections and the seven consensus functions.
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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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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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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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