TESI DOCTORAL - La Salle

C.4. Computationally optimal RHCA, DHCA and flat consensus comparison
Running time comparison
The characteristics of this data set –in particular, the low cardinalities of the diversity factors
associated to it–, make flat consensus the fastest consensus architecture when compared to
the serial implementations of RHCA and DHCA regardless of the diversity scenario —see
figure C.44. The only exception is the MCLA consensus function, whose time complexity
scales quadratically with the size of the ensemble, which penalizes the execution of one-step
consensus processes in front of their hierarchical counterparts.
A somewhat lighter version of this same behaviour is observed in the running time
analysis of the parallel implementations of the HCA, which is presented in figure C.45. In
this case, though, RHCA and DHCA are as fast or faster than flat consensus when the
HGPA, MCLA and KMSAD consensus functions are employed.
Consensus quality comparison
As regards the quality of the consensus clustering solutions output by the three consensus
architectures, figure C.46 shows the results obtained on the Balance data collection. It is
to notice that the EAC and HGPA consensus functions yield, in general, the lowest quality
results. For the remaining consensus functions, pretty similar quality consensus solutions are
obtained by means of the three architectures, except for the ALSAD and SLSAD consensus
function, where notable differences are observed between HCA and flat consensus.
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