TESI DOCTORAL - La Salle

clustering count
clustering count
10
5
Mfeat FAC
0
0 0.5 1
φ (NMI)
10
5
(a) FAC
Mfeat MOR
0
0 0.5 1
φ (NMI)
(d) MOR
Appendix B. Experiments on clustering indeterminacies
clustering count
clustering count
10
5
Mfeat FOU
0
0 0.5 1
φ (NMI)
10
5
(b) FOU
Mfeat PIX
0
0 0.5 1
φ (NMI)
(e) PIX
clustering count
clustering count
10
5
Mfeat KAR
0
0 0.5 1
φ (NMI)
10
5
(c) KAR
Mfeat ZER
0
0 0.5 1
φ (NMI)
(f) ZER
Figure B.8: Histograms of the φ (NMI) values obtained on each data representation in the
MFeat data set.
clustering count
60
40
20
miniNG Baseline
0
0 0.5 1
φ (NMI)
(a) Baseline
clustering count
60
40
20
miniNG PCA
0
0 0.5 1
φ (NMI)
(b) PCA
clustering count
60
40
20
miniNG ICA
0
0 0.5 1
φ (NMI)
(c) ICA
clustering count
60
40
20
miniNG NMF
0
0 0.5 1
φ (NMI)
(d) NMF
clustering count
60
40
20
miniNG RP
0
0 0.5 1
φ (NMI)
(e) RP
Figure B.9: Histograms of the φ (NMI) values obtained on each data representation in the
miniNG data set.
Furthermore, if figures B.10(b) and B.10(c) are compared to figure B.10(d), it is easy to
see that whereas the two former present a wide and sharp peak centered around φ (NMI) =0.7
(thus indicating that clustering solutions this good are likely to be obtained using the PCA
and ICA representations of the objects), the latter has its acme around φ (NMI) =0.35—i.e.
the quality of the RP based clustering solutions tends to be lower in this data set.
B.1.11 BBC data set
The BBC data collection constitutes another example where very diverse clustering solutions
–with qualities ranging from φ (NMI) =0.01 to φ (NMI) =0.81– are obtained when clustering
is conducted on the original representation of the objects (see figure B.11(a)).
As far as the remaining data representations are concerned, the best results seem to
be obtained using the NMF feature extraction technique, as its corresponding histogram is
more scarcely and densely populated at the low and high ranges of φ (NMI) , respectively.
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