TESI DOCTORAL - La Salle

Appendix C. Experiments on hierarchical consensus architectures
attribute. Moreover, flat consensus is unfeasible even when based on the aforementioned
consensus functions.
Running time comparison
Figure C.59 shows the running times corresponding to the serial implementation of RHCA
and DHCA. It can be observed that, as the cluster ensemble size increases, RHCA becomes
faster than DHCA. This is due to the fact that this growth is induced by an augmentation of
the cardinality of the algorithmic diversity factor |dfA|, which directly produces an increase
of the time complexity of one of the DHCA stages, while the impact of this growth is
somewhat scattered across the distinct stages of a RHCA.
Approximately the same behaviour is observed when the parallel implementation of both
hierarchical consensus architectures is analyzed —see figure C.60.
Consensus quality comparison
Figure C.61 presents the φ (NMI) corresponding to the two aforementioned consensus architectures.
There are a couple of issues worth noting in this case. Firstly, notice that HGPA
yields very poor consensus clustering solutions on this data collection (recall that this fact
has also been reported in several of the previous data sets). And secondly, it is important to
highlight the notable differences between the quality of the consensus clusterings output by
RHCA and DHCA, as the latter consensus architecture tends to yield far better consensus
clustering solutions than the former —a trend that becomes more evident in high diversity
scenarios.
329