TESI DOCTORAL - La Salle

B.1. Clustering indeterminacies in unimodal data sets
B.1.3 Wine data set
The histograms of the φ (NMI) values obtained by each clustering algorithm across all the
data representations employed in the Wine data set are presented in figure B.3.
clustering count
40
30
20
10
Wine Baseline
0
0 0.5 1
φ (NMI)
(a) Baseline
clustering count
40
30
20
10
Wine PCA
0
0 0.5 1
φ (NMI)
(b) PCA
clustering count
40
30
20
10
Wine ICA
0
0 0.5 1
φ (NMI)
(c) ICA
clustering count
40
30
20
10
Wine NMF
0
0 0.5 1
φ (NMI)
(d) NMF
clustering count
40
30
20
10
Wine RP
0
0 0.5 1
φ (NMI)
(e) RP
Figure B.3: Histograms of the φ (NMI) values obtained on each data representation in the
Wine data set.
The clustering indeterminacy regarding the selection of both the clustering algorithm
and the dimensionality of the data representation is clearly observed in figures B.3(b) and
B.3(c). For both the PCA and ICA data representations, a rather even histogram is obtained,
spanning from φ (NMI) =0.04 to φ (NMI) =0.84.
Moreover, notice that it is only with these data representations (PCA and ICA) that
φ (NMI) values above 0.5 are obtained on this data set, which reinforces the importance of
using the optimal type of features for the obtention of good clustering results.
B.1.4 Glass data set
The φ (NMI) histograms corresponding to the Glass data set are presented in figure B.4.
clustering count
25
20
15
10
5
Glass Baseline
0
0 0.5 1
φ (NMI)
(a) Baseline
clustering count
25
20
15
10
5
Glass PCA
0
0 0.5 1
φ (NMI)
(b) PCA
clustering count
25
20
15
10
5
Glass ICA
0
0 0.5 1
φ (NMI)
(c) ICA
clustering count
25
20
15
10
5
Glass NMF
0
0 0.5 1
φ (NMI)
(d) NMF
clustering count
25
20
15
10
5
Glass RP
0
0 0.5 1
φ (NMI)
(e) RP
Figure B.4: Histograms of the φ (NMI) values obtained on each data representation in the
Glass data set.
Notice the distinct histogram distributions obtained for each data representation, which
gives an idea of how the selection of a particular data representation influences the quality of
the clustering results. Additionally, a pretty wide range of values of φ (NMI) are observed in
the histograms corresponding to the feature extraction based data representations (figures
B.4(b) to B.4(e)), thus evidencing the effect of the dimensionality reduction and clustering
algorithm selection indeterminacy.
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