TESI DOCTORAL - La Salle

Chapter 3. Hierarchical consensus architectures
in those data sets having small cluster ensembles even in high diversity scenarios (e.g. Iris,
Balance or MFeat).
Thirdly, as the number of objects n contained in the data set increases (such as in
PenDigits collection), only the HGPA and MCLA consensus functions are executable (as
they are the only whose time complexity scales linearly with the data set size, see appendix
A.5), and hierarchical consensus architectures are the most computationally efficient ones.
However, if the data set was even larger, nor flat neither DHCA would be affordable from a
computational perspective —with the resources employed in our experiments, see appendix
A.6.
Most of these observations can be extrapolated to the case of the fully parallel consensus
implementation (table 3.11), where a pretty overwhelming prevalence of DHCA variants over
flat consensus can be observed, a trend that was already reported earlier in this section and
can also be observed in the experiments described in appendix C.3.
For brevity reasons, the experiments presented in the remains of this work concerning
deterministic hierarchical consensus architectures will solely refer to those DHCA variants
of minimum estimated running time —i.e. those presented in tables 3.10 and 3.11.
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