TESI DOCTORAL - La Salle

Appendix D
Experiments on self-refining
consensus architectures
This appendix presents several experiments regarding self-refining flat and hierarchical consensus
architectures described in chapter 4. In appendix D.1, the proposal for automatically
refining a previously derived consensus clustering solution –what is called consensus based
self-refining– is experimentally evaluated, whereas appendix D.2 presents the experiments
regarding the creation of a refined consensus clustering solution upon the selection of a high
quality cluster ensemble component, i.e. selection-based self-refining.
In both cases, the experiments are conducted on eleven unimodal data collections,
namely: Iris, Wine, Glass, Ionosphere, WDBC, Balance, MFeat, miniNG, Segmentation,
BBC and PenDigits. The results of the self-refining experiments are displayed by means
of boxplot charts showing the normalized mutual information (φ (NMI) ) with respect to the
ground truth of each data set compiled across 100 independent experiment runs of i) the
cluster ensemble E each experiment is conducted upon, ii) the clustering solution employed
as the reference of the self-refining procedure, and iii) the self-refined consensus cluster-
ing solutions λc p i obtained upon select cluster ensembles Epi
created by the selection of
a percentage pi = {2, 5, 10, 15, 20, 30, 40, 50, 60, 75, 90} of the whole ensemble E. Asinall
the experimental sections of this thesis, consensus processes have been replicated using the
set of seven consensus functions described in appendix A.5, namely: CSPA, EAC, HGPA,
MCLA, ALSAD, KMSAD and SLSAD.
D.1 Experiments on consensus-based self-refining
In this section, the results of applying the consensus-based self-refining procedure described
in section 4.1 on the aforementioned eleven data sets are presented. The self-refining process
is intended to better the quality of a consensus clustering solution λc output by a flat, a
random (RHCA) and a deterministic hierarchical consensus architecture (DHCA).
For this reason, the results are displayed as a matrix of boxplot charts with three columns
(the leftmost one for flat consensus, the central one presenting the results of RHCA, and
DHCA on the right), and as many rows as consensus functions are employed on each
particular data collection (seven in all cases except for the PenDigits data set, where only
two consensus functions are applicable due to memory limitations given our computational
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