TESI DOCTORAL - La Salle

– What do we want to measure?
Chapter 4. Self-refining consensus architectures
i) The quality of the self-refined consensus clusterings obtained by the proposed
methodology when applied on the consensus clusterings output by the flat and
allegedly fastest RHCA and DHCA consensus architectures.
ii) The ability of the proposed self-refining procedure to obtain a consensus clustering
of higher quality than that of a) its non-refined counterpart, and b) the
highest and median quality cluster ensemble components.
iii) The quality of the self-refined consensus clustering of maximum quality compared
to its non-refined counterpart.
iv) The ability of the supraconsensus function to select, in a fully unsupervised
manner, the highest quality self-refined consensus clustering among the set of
self-refined clusterings generated.
v) We analyze whether self-refining constitutes a means for uniformizing the quality
of the consensus clustering solutions yielded by the flat and allegedly fastest
RHCA and DHCA consensus architectures, thus making it possible to decide, on
computational grounds only, which is the most suitable consensus architecture
for a given clustering problem.
– How do we measure it?
i) The quality of the self-refined consensus clusterings is measured in terms of the
φ (NMI) with respect to the ground truth of each data collection.
ii) The percentage of experiments in which the proposed self-refining procedure gives
rise to at least one self-refined consensus clustering of higher quality than that
of a) its non-refined counterpart, and b) the highest and median quality cluster
ensemble components.
iii) We measure the relative φ (NMI) percentage difference between the self-refined
consensus clustering of maximum quality and its non-refined counterpart.
iv) The precision of the supraconsensus function is measured in terms of the percentage
of experiments in which it manages to select the highest quality self-refined
consensus clustering.
v) We compare the average variance between the φ (NMI) scores of the consensus
clusterings λc yielded by the three evaluated consensus architectures (i.e. prior to
self-refining) with the variance between the consensus clustering selected by the
) after the self-refining procedure is conducted.
supraconsensus function (λ final
c
– How are the experiments designed? we only analyze the results of the consensus
self-refining process executed on the highest diversity scenario (i.e. the one where
cluster ensembles are created by applying the |dfA| = 28 clustering algorithms from
the CLUTO clustering package). The reason for this is twofold: besides brevity, this
limitation on our analysis avoids that the results of the self-refining process are masked
by the consensus quality variability observed in lower diversity scenarios —recall that,
in those cases, the quality of the consensus clustering solutions shows larger variances,
as the cluster ensemble changes from experiment to experiment due to the random
selection of |dfA| = {1, 10, 19} clustering algorithms, whereas exactly the same cluster
ensemble is employed across the ten experiments in the highest diversity scenario. As
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