TESI DOCTORAL - La Salle

F.1. Iris data set
φ (NMI)
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IRIS
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CPU time (sec.)
CSPA
EAC
HGPA
MCLA
VMA
BC
CC
PC
SC
Figure F.1: φ (NMI) vs CPU time mean ± 2-standard deviation regions of the soft consensus
functions on the Iris data collection.
F.1 Iris data set
This section described the results of the soft consensus clustering experiments run on the
Iris data set. Figure F.1 presents the φ (NMI) vs CPU time mean ± 2-standard deviation
regions of the nine consensus functions compared. Quite obviously, the closer the scatterplot
of a consensus function was to the top left corner of the diagram, the better its performance
would be (i.e. it would yield high quality consensus clustering solutions with low time
complexity).
In this case, the proposed SC and PC consensus functions match the performance of the
VMA, both in terms of time complexity and consensus quality. The performance of the other
two consensus functions proposed (BC and CC) is pretty comparable as far as the quality
of the consensus clustering solutions is concerned, but their computational complexity is
higher. As regards the state-of-the-art consensus functions, CSPA seems to yield slightly
better quality results, although its CPU time more than doubles our proposals, being the
most costly. On its part, MCLA seems to be competitive from a computational viewpoint,
but it yields lower quality consensus clusterings. Last, EAC and HGPA are the worst
performing consensus functions.
If the statistical significance of the results is evaluated –see table F.1–, it can be obvserved
that the φ (NMI) superiority of CSPA is only apparent, as the differences with respect
to BC and CC are not statistically significant, and the quality of the consensus clusterings
output by SC and PC are significantly better than those of CSPA. Moreover, SC and PC
are statistically equivalent to VMA both in terms of quality and execution time.
F.2 Wine data set
The soft consensus clustering results obtained on the Wine data collection are presented
next. For starters, figure F.2 displays the φ (NMI) vs CPU time mean ± 2-standard deviation
regions corresponding to the nine consensus functions compared. In general terms, it can
be observed that VMA is the fastest alternative, while the best quality consensus clustering
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