TESI DOCTORAL - La Salle

3.4. Flat vs. hierarchical consensus
tends to be a bit quicker to execute than DHCA in high diversity scenarios, although the
fact that the mini-ensembles sizes employed in the fastest random architecture variants are
usually larger than those employed in DHCA is penalized by consensus architectures based
on the MCLA consensus function. And last, as already perceived in the serial case, RHCA
tends to be slightly more efficient than DHCA when clustering combination is conducted by
means of consensus functions based on treating hierarchical clustering similarity measures
as data (i.e. ALSAD and SLSAD).
To sum up, we can conclude that there exists a very important dependence between
the computationally optimal type of consensus architecture, the size of the cluster ensemble
upon which consensus is built and the consensus function employed. From a practical
standpoint, in front of a specific consensus clustering problem (i.e. a cluster ensemble of a
given size l and a particular computational resources configuration), the user should take
into account how these factors interact at the time of deciding which type of consensus
architecture is to be implemented. However, this decision should not only be made on
computational efficiency grounds. In fact, it should also allow for the quality of the consensus
clustering solution obtained, as the quick obtention of a poor consensus data grouping
would be of little use in practice. For this reason, the next section evaluates the quality of
the consensus label vectors output by the same consensus architectures that have just been
analyzed in computational terms.
3.4.2 Consensus quality comparison
In this section, we evaluate the quality of the consensus clustering solutions yielded by the
fastest DHCA and RHCA variants and flat consensus architectures, which constitutes an
indicator of their suitability for conducting robust clustering. The experiments conducted
to this end follow the design described next.
Experimental design
– What do we want to measure?
i) The suitability of the allegedly fastest DHCA and RHCA variants and flat consensus
for obtaining clustering results robust to the inherent indeterminacies of
clustering.
ii) A further goal of this section is to determine whether certain consensus architectures
tend to outperform others as regards the quality of the consensus clusterings
they obtain.
– How do we measure it?
i) We analyze the quality of the consensus clustering solutions obtained by these
consensus architectures, comparing it with respect the individual clusterings contained
in the cluster ensemble E upon which consensus is built. The more similar
the qualities of the consensus clustering solution and the top quality cluster ensemble
components, the higher robustness to the clustering indeterminacies is
attained. As mentioned in section 1.2.2, in this work we evaluate clustering
solutions by means of an external cluster validity index, i.e. we compare the consensus
clustering solution embodied in the labeling vector λc with a predefined
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