TESI DOCTORAL - La Salle

C.4. Computationally optimal RHCA, DHCA and flat consensus comparison
Consensus quality comparison
The φ (NMI) values of the consensus clustering solutions yielded by flat, random hierarchical
and deterministic hierarhical consensus architectures follows a pattern that is quite similar
to what has been observed in the previous data collections, at least as far as the performance
of the distinct consensus functions is concerned. That is, the lowest quality consensus solutions
are obtained by means of the EAC and HGPA consensus functions, whereas ALSAD
tends to yield the best results.
C.4.10 BBC data set
In this section, the running time and consensus quality comparison experiments are conducted
on the BBC data collection. The four diversity scenarios correspond to cluster
ensembles of sizes l =57, 570, 1083 and 1596.
Running time comparison
As far as the running times of the entirely serial implementation of RHCA and DHCA
and of flat consensus are concerned, the boxplots depicted in figure C.56 show that flat
consensus constitutes the most computationally competitive consensus architecture in most
cases —in fact, the only exceptions occur when the HGPA and MCLA consensus functions
are employed.
When the parallel implementation of hierarchical consensus architecture is considered,
they become more competitive (in computational terms), reverting the situation observed
in the serial case for the CSPA and KMSAD consensus functions —see figure C.57.
Consensus quality comparison
As regards the quality of the consensus clustering solutions yielded by the three consensus
architectures (measured as the φ (NMI) with respect to the ground truth that defines the
true group structure of the BBC data collection), we can observe great differences between
the performance of the distinct consensus functions –see figure C.58–: while the MCLA,
ALSAD and KMSAD clustering combiners tend to yield consensus clusterings of quality
comparable to the best components of the cluster ensemble, the clustering solutions output
by consensus architectures based on EAC, HGPA and SLSAD are notably poorer.
C.4.11 PenDigits data set
This section presents the execution times of the computationally optimal RHCA, DHCA
and flat consensus architecture and the φ (NMI) values of the consensus clustering solutions
yielded by them on the PenDigits data collection. The presented results consider the
experiments conducted across four diversity scenarios, and the cluster ensemble sizes corresponding
to them are l =57, 570, 1083 and 1596, respectively. Due to the number of objects
contained in this data set, only the HGPA and MCLA consensus functions are executable
on it, as they are the only ones the space complexity of which scales linearly with this
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