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B.2. Clustering indeterminacies in multimodal data sets clustering count clustering count clustering count 80 60 40 20 CAL500 Baseline 0 0 0.5 1 φ (NMI) (a) Baseline (multimodal) CAL500 Baseline M1 80 60 40 20 0 0 0.5 φ 1 (NMI) (e) Baseline (audio) CAL500 Baseline M2 80 60 40 20 0 0 0.5 φ 1 (NMI) (i) Baseline (text) clustering count clustering count clustering count CAL500 PCA 80 60 40 20 0 0 0.5 φ 1 (NMI) (b) PCA (multimodal) CAL500 PCA M1 80 60 40 20 0 0 0.5 φ 1 (NMI) (f) PCA (audio) CAL500 PCA M2 80 60 40 20 0 0 0.5 φ 1 (NMI) (j) PCA (text) clustering count clustering count clustering count CAL500 ICA 80 60 40 20 0 0 0.5 φ 1 (NMI) (c) ICA (multimodal) CAL500 ICA M1 80 60 40 20 0 0 0.5 φ 1 (NMI) (g) ICA (audio) CAL500 ICA M2 80 60 40 20 0 0 0.5 φ 1 (NMI) (k) ICA (text) clustering count clustering count clustering count CAL500 RP 80 60 40 20 0 0 0.5 φ 1 (NMI) (d) RP (multimodal) CAL500 RP M1 80 60 40 20 0 0 0.5 φ 1 (NMI) (h) RP (audio) CAL500 RP M2 80 60 40 20 0 0 0.5 φ 1 (NMI) (l) RP (text) Figure B.13: Histograms of the φ (NMI) values on the CAL500 data set obtained on the following data representations. which are far better than those obtained on the text modality (always below φ (NMI) =0.3). <strong>La</strong>st but not least, the multimodal object representation seems to benefit slightly from the early fusion of the visual and textual features of both modalities, as better clustering results are obtained in this case, although by a very small margin. B.2.3 InternetAds data set The clustering results corresponding to the InternetAds data collection are summarized in figure B.15. Many poor clustering results are obtained on this data set, as the high peaks located on the leftmost regions of the histograms reveal. The distinct data representations and modalities present a pretty erratic behaviour, as discussed next. If the two modalities are compared, the best clustering results are obtained, in general terms, using the collateral information of the Internet advertisements (which are the objects in this data set). However, the multimodal composition of the objects tends to yield superior –although still poor– quality clusterings, except for the PCA representation. 244
clustering count clustering count clustering count 60 40 20 Corel Baseline 0 0 0.5 1 φ (NMI) (a) Baseline (multimodal) 60 40 20 Corel Baseline M1 0 0 0.5 1 φ (NMI) (f) Baseline (image) 60 40 20 Corel Baseline M2 0 0 0.5 1 φ (NMI) (k) Baseline (text) clustering count clustering count clustering count 60 40 20 Corel PCA 0 0 0.5 1 φ (NMI) (b) PCA (multimodal) 60 40 20 Corel PCA M1 0 0 0.5 1 φ (NMI) (g) PCA (image) 60 40 20 Corel PCA M2 0 0 0.5 1 φ (NMI) (l) PCA (text) Appendix B. Experiments on clustering indeterminacies clustering count clustering count clustering count 60 40 20 Corel ICA 0 0 0.5 1 φ (NMI) (c) ICA (multimodal) 60 40 20 Corel ICA M1 0 0 0.5 1 φ (NMI) (h) ICA (image) 60 40 20 Corel ICA M2 0 0 0.5 1 φ (NMI) (m) ICA (text) clustering count clustering count clustering count 60 40 20 Corel NMF 0 0 0.5 1 φ (NMI) (d) NMF (multimodal) 60 40 20 Corel NMF M1 0 0 0.5 1 φ (NMI) (i) NMF (image) 60 40 20 Corel NMF M2 0 0 0.5 1 φ (NMI) (n) NMF (text) clustering count clustering count clustering count 60 40 20 Corel RP 0 0 0.5 1 φ (NMI) (e) RP (multimodal) 60 40 20 Corel RP M1 0 0 0.5 1 φ (NMI) 60 40 20 (j) RP (image) Corel RP M2 0 0 0.5 1 φ (NMI) (o) RP (text) Figure B.14: Histograms of the φ (NMI) values on the Corel data set obtained on the following data representations. B.2.4 IsoLetters data set The collection of clustering solutions obtained on the IsoLetters artificial multimodal data collection are presented representation and modality-wise in figure B.16. In this case, the quality of the clusterings created on the distinct object representations present two clearly different histogram patterns depending on the modality. For instance, in the speech modality (figures B.16(e) to B.16(h)), the baseline and RP histograms present a main and a secondary minor peaks, whereas the PCA and ICA representations yield a pretty uniform distribution of clusterings. In contrast, a totally different distribution is found when clustering is run on the visual mode, where a single negatively skewed bell shape is observed (see figures B.16(i) to B.16(l)). Finally, it is to note that, regardless of the object representation employed, the early fusion of the speech and visual features of this data set gives rise to a notable increase in the quality of the clustering results (a 16.2% in averaged relative terms as regards the top quality individual clustering solution). 245
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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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1.2. Clustering in knowledge discov
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Chapter 2. Cluster ensembles and co
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fuzzy consensus clustering solution
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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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5.5. Related publications modes joi
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Chapter 6. Voting based consensus f
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6.4. Experiments CSPA EAC HGPA MCLA
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Page 247 and 248: References Li, S.Z. and G. GuoDong.
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E.1. CAL500 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.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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