TESI DOCTORAL - La Salle

3.2. Random hierarchical consensus architectures
running time. Secondly, notice that flat consensus tends to be computationally optimal in
those data sets having small cluster ensembles even in high diversity scenarios (e.g. Iris,
Balance or MFeat). Thirdly, for data collections containing a large number of objects n
(such as PenDigits), only the HGPA and MCLA consensus functions are executable in our
experimental conditions (as they are the only whose complexity scales linearly with the
data set size, see appendix A.5). And last, notice the predominance of RHCA variants
with s =2ands = 3 stages among the fastest ones, which seems to indicate that, from a
computational perspective, RHCA variants few stages are more efficient, despite consensus
processes are conducted on large mini-cluster ensembles.
Most of these observations can be extrapolated to the case of the fully parallel consensus
implementation (table 3.5), where we can observe a pretty overwhelming prevalence of
RHCA variants over flat consensus, a trend that was already reported earlier in this section
and also in appendix C.2.
In the remains of this work, experiments concerning random hierarchical consensus
architectures have been limited to those RHCA variants of minimum estimated running
time for the sake of brevity.
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