TESI DOCTORAL - La Salle

clustering count
clustering count
clustering count
30
20
10
IsoLetters Baseline
0
0 0.5 1
φ (NMI)
(a) Baseline
(multimodal)
30
20
10
IsoLetters Baseline M1
0
0 0.5 1
φ (NMI)
(e) Baseline
(speech)
30
20
10
IsoLetters Baseline M2
0
0 0.5 1
φ (NMI)
(i) Baseline
(image)
clustering count
clustering count
clustering count
30
20
10
Appendix B. Experiments on clustering indeterminacies
IsoLetters PCA
0
0 0.5 1
φ (NMI)
(b) PCA (multimodal)
30
20
10
IsoLetters PCA M1
0
0 0.5 1
φ (NMI)
(f) PCA
(speech)
30
20
10
IsoLetters PCA M2
0
0 0.5 1
φ (NMI)
(j) PCA (image)
clustering count
clustering count
clustering count
30
20
10
IsoLetters ICA
0
0 0.5 1
φ (NMI)
(c) ICA (multimodal)
30
20
10
IsoLetters ICA M1
0
0 0.5 1
φ (NMI)
(g) ICA
(speech)
30
20
10
IsoLetters ICA M2
0
0 0.5 1
φ (NMI)
(k) ICA (image)
clustering count
clustering count
clustering count
30
20
10
IsoLetters RP
0
0 0.5 1
φ (NMI)
(d) RP (multimodal)
30
20
10
IsoLetters RP M1
0
0 0.5 1
φ (NMI)
(h) RP (speech)
30
20
10
IsoLetters RP M2
0
0 0.5 1
φ (NMI)
(l) RP (image)
Figure B.16: Histograms of the φ (NMI) values on the IsoLetters data set obtained on the
following data representations.
representations dominate the best clustering results across all the families of algorithms,
whereas it is one of the unimodal representations the ones to do so in the CAL500 and InternetAds
collections. And finally, notice the diversity of types of representations appearing
in table B.4, which suggests that, for a given data set, it is very difficult to select the data
representation and clustering strategy that yield the best clustering results.
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