TESI DOCTORAL - La Salle

D.1. Experiments on consensus-based self-refining
D.1.7 MFeat data set
Figure D.7 presents the boxplots of the clusterings resulting from running the consensus
self-refining procedure on the MFeat data collection. In this case, pretty varied behaviours
are observed. For instance, when a high quality consensus clustering solution λc is available
prior to self-refining, none of the refined consensus clusterings achieves a higher φ (NMI) —
see, for instance, the boxplots corresponding to the CSPA, ALSAD and KMSAD consensus
functions. In contrast, in cases in which λc has a low φ (NMI) , self-refining brings about
sometimes notable quality gains, such as the ones observed in the EAC or SLSAD based
flat and RHCA consensus architectures. However, the supraconsensus function tends to
select the non-refined clustering solution as the final partition of the process in a majority
of cases.
D.1.8 miniNG data set
The boxplot charts depicted in figure D.8 summarize the performance of the consensusbased
self-refining procedure when applied on the miniNG data set. It is interesting to
note that, except when self-refining is based on the EAC consensus function, important
quality gains are obtained —in most cases, there exists at least one self-refined consensus
clustering with higher φ (NMI) than the non-refined clustering λc. Notice the large quality
gains obtained when self-refining is based on MCLA, as we move from a very low φ (NMI)
non-refined consensus clustering solution λc to self-refined clusterings that are comparable
to the highest quality components in the cluster ensemble E. However, when self-refining is
basedonEAC,little(ifany)φ (NMI) increases are introduced by the self-refining procedure.
Last, notice how, in most cases, the supraconsensus function selects high quality clustering
solutions as the final partition.
D.1.9 Segmentation data set
Figure D.9 presents the boxplots of the non-refined and self-refined consensus clustering
solutions obtained by the consensus-based self-refining procedure applied on the Segmentation
data collection. Notice that, thanks to the proposed refining process, at least one
self-refined clustering solution of higher quality than that of the non-refined consensus clustering
λc is obtained in most cases. In fact, the only exceptions occur in the refinement of
the λc output by the flat and DHCA consensus architecture based on the EAC consensus
function.
As regards the performance of the supraconsensus function, we can see that it casts a
shadow over the good results of the self-refining process just reported, as it rarely picks up
the highest quality consensus clustering solution —although it usually selects one of the
higher quality ones.
D.1.10 BBC data set
The qualities of the clusterings resulting from applying the consensus-based self-refining
procedure on the BBC data set are presented in the boxplots of figure D.10. Notice that,
although the quality of the non-refined consensus clustering λc is highly dependent on
the consensus function employed (from the high φ (NMI) values in CSPA, MCLA, ALSAD
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