TESI DOCTORAL - La Salle

D.1. Experiments on consensus-based self-refining
resources —see appendix A.6).
Moreover, the clustering solution deemed as the optimal by the supraconsensus function
described in section 4.1 in a majority of experiment runs is highlighted by means of a vertical
green dashed line, so that its performance can be qualitatively evaluated at a glance.
D.1.1 Iris data set
Figure D.1 presents the results of the self-refining consensus procedure applied on the Iris
data set. As regards the results obtained using the CSPA consensus function, we can see
that self-refining introduces no variations with respect to the quality of the non-refined
consensus clustering solution λc in the case of the flat and RHCA consensus architectures.
In contrast, slight but important φ (NMI) gains are obtained in the case of DHCA with the
refined clustering solutions λ c 20 and λ c 40. Unfortunately, the supraconsensus function fails
to select in this case one of the highest quality clustering solutions. A very similar situation is
observed on the self-refining experiments based on the EAC, ALSAD and SLSAD consensus
functions.
Examples in which self-refining and supraconsensus perform successfully are the ones
regarding both hierarchical consensus architectures using the HGPA consensus function. In
this cases, self-refined consensus clustering solutions of higher quality than the one of λc
are obtained and selected by the supraconsensus function. In contrast, in the experiments
basedonMCLA,little(ifany)φ (NMI) gains are obtained via refining, and supraconsensus
tends to select a clustering solution of slightly lower quality than λc.
D.1.2 Wine data set
The results corresponding to the application of the consensus-based self-refining procedure
on the Wine data set are depicted in figure D.2. As far as the refining of the consensus
solution output by the flat consensus architecture (leftmost column of figure D.2), we can
see that quality improvements with respect to the initial consensus clustering λc are obtained
in all cases, except when the HGPA consensus function is employed. In some cases,
supraconsensus manages to select the highest quality clustering, such as when consensus
is based on MCLA and SLSAD, whereas suboptimal solutions are selected in other cases
—see for instance the CSPA, EAC, HGPA and ALSAD boxplots.
We would like to hihglight the specially good results obtained on the self-refining of
the consensus output by the DHCA architecture, see the rightmost column of figure D.2.
Regardless of the consensus function employed, the self-refining procedure gives rise to
higher quality clustering solutions, and the supraconsensus function selects the top quality
one in most cases.
D.1.3 Glass data set
Figure D.3 presents the results of the consensus self-refining process when applied on the
Glass data collection. In this case, little φ (NMI) gains are obtained by self-refining for most
consensus functions. The clearest exception is EAC, where notable quality increases are
observed, specially when self-refining is applied on the consensus solutions output by the
hierarchical consensus architectures (RHCA and DHCA).
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