TESI DOCTORAL - La Salle

B.1. Clustering indeterminacies in unimodal data sets
B.1.7 Balance data set
The approximately even distributions of the φ (NMI) histograms corresponding to the four
object representations employed in the Balance data set (with the exception of the peak
around φ (NMI) =0.04 in figure B.7(c)) transmit the idea that the chance of randomly selecting
a good or a bad clustering configuration is rather equiprobable in this data collection.
clustering count
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Balance Baseline
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φ (NMI)
(a) Baseline
clustering count
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Balance PCA
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φ (NMI)
(b) PCA
clustering count
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Balance NMF
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(c) NMF
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Balance RP
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(d) RP
Figure B.7: Histograms of the φ (NMI) values obtained on the each data representation in
the Balance data set.
B.1.8 MFeat data set
In this data set, six distinct feature types were employed for representing the objects, each
with a single dimensionality. Therefore, the φ (NMI) scatter observed in each of the figures
from B.8(a) to B.8(f) is solely due to the algorithm selection indeterminacy.
Notice that, in all these histograms, a pretty high density of clustering solutions around
φ (NMI) =0.5 can be observed. Nevertheless, notably better clustering results (φ (NMI) ≈ 0.8)
can be obtained using the KAR and PIX object representations (see figures B.8(c) and
B.8(e)), which reveals the data representation indeterminacy effect.
B.1.9 miniNG data set
The wide spread of the φ (NMI) values observed in figure B.9(a) –from φ (NMI) =0.06 to
φ (NMI) =0.64– is a clear evidence of how the selection of a particular clustering algorithm
affects the quality of the clustering results.
Moreover, notice that the clustering solutions obtained on the RP representation yield
φ (NMI) values below 0.3, whereas the best results obtained on the remaining representations
reach and even surpass φ (NMI) =0.5 —i.e. distinct object representations can significantly
alter the results of a clustering process.
B.1.10 Segmentation data set
As regards the effect of applying distinct clustering algorithms on the same object representation,
figure B.10(a) shows how, despite the accumulation of clustering solutions around
φ (NMI) =0.35, a maximum quality of φ (NMI) =0.65 can be obtained on the baseline represenmtation
of the objects.
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