TESI DOCTORAL - La Salle

Appendix C. Experiments on hierarchical consensus architectures
DHCA variant implemented, at least, no significant differences are observed among them –
as opposed to the serial implementation case–, while hierarchical consensus is more efficient
than flat consensus as soon as the cluster ensemble size grows.
C.3.6 Balance data set
This section presents the results of estimating the execution times of the fully serial and
parallel implementations of DHCA in the four diversity scenarios for the Balance data set,
which give rise to cluster ensembles of sizes l =7, 70, 133 and 196 each.
Firstly, figure C.25 presents the estimated and real execution times corresponding to
the serial implementation context. It is quite apparent that SERTDHCA provides the user
with a good estimation of the real running time of consensus architectures (SRTDHCA) and,
as such, it allows determining which is the computationally optimal consensus architecture
with a high degree of accuracy. In this case, given the small size of the cluster ensemble
in either of the four diversity scenarios, flat consensus is faster than most serial DHCA
variants.
And secondly, the results corresponding to the parallel implementation of DHCA are
depicted in figure C.26. Once more, all DHCA variants have very similar running times.
As in the serial case, flat consensus is faster than any of its hierarhical counterparts, except
when the HGPA and MCLA consensus functions are employed as clustering combiners.
C.3.7 MFeat data set
This section describes the results of the minimum complexity DHCA variant selection based
on running time estimation. In the case of the MFeat data collection, cluster ensembles of
sizes l =6, 60, 114 and 168 correspond to the four diversity scenarios where this experiment
is conducted.
Figure C.27 depicts the estimated and real execution times of the fully serial implementation
of DHCA. In this case, SERTDHCA is a pretty accurate estimator of SRTDHCA, and,
as such, it is a good predictor of the most computationally efficient consensus architecture.
In most cases, however, due to the relatively small sizes of the cluster ensembles in this data
set, flat consensus is faster than any of the DHCA variants —except when the HGPA and
MCLA consensus functions are employed in high diversity scenarios.
Last, figure C.28 presents the results corrresponding to the parallel DHCA implementation.
Again, the time complexities of DHCA variants reach pretty similar values. However,
notice that DHCA variants are slower than flat consensus in most of the diversity scenarios
—except when the HGPA and MCLA consensus functions are used for combining the
clusterings.
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