TESI DOCTORAL - La Salle

SERT RHCA (sec.)
PERT RHCA (sec.)
10
10
6 5 5 4 4 3 3 2 2 1
2
s : number of stages
10 1
10 0
10 1
10 0
10 −1
2 3 4 5 6 8 9 26 27 541 1083
b : mini−ensemble size
(a) Serial estimated running time
s : number of stages
10 6 5 5 4 4 3 3 2 2 1
2 3 4 5 6 8 9 26 27 541 1083
b : mini−ensemble size
(c) Parallel estimated running time
CSPA
EAC
HGPA
MCLA
ALSAD
KMSAD
SLSAD
CSPA
EAC
HGPA
MCLA
ALSAD
KMSAD
SLSAD
Chapter 3. Hierarchical consensus architectures
SRT RHCA (sec.)
PRT RHCA (sec.)
10
10
6 5 5 4 4 3 3 2 2 1
2
s : number of stages
10 1
10 0
10 1
10 0
10 −1
2 3 4 5 6 8 9 26 27 541 1083
b : mini−ensemble size
(b) Serial real running time
s : number of stages
10 6 5 5 4 4 3 3 2 2 1
2 3 4 5 6 8 9 26 27 541 1083
b : mini−ensemble size
(d) Parallel real running time
CSPA
EAC
HGPA
MCLA
ALSAD
KMSAD
SLSAD
CSPA
EAC
HGPA
MCLA
ALSAD
KMSAD
SLSAD
Figure 3.6: Estimated and real running times of the serial RHCA on the Zoo data collection
in the diversity scenario corresponding to a cluster ensemble of size l = 1083.
scenario, there exists a notable difference between the running times of the most efficient
parallel RHCA and flat consensus, which can be as high as two orders of magnitude.
Diversity scenario |df A| =28
Figure 3.7 depicts the estimated and real execution times corresponding to the highest
diversity scenario —i.e. the one resulting from applying the |dfA| = 28 clustering algorithms
from the CLUTO clustering package for generating cluster ensembles of size l = 1596. In
this case, the mini-ensembles size sweep is b = {2, 3, 4, 5, 6, 10, 11, 32, 33, 798, 1596}.
The results obtained are pretty similar to those obtained on the previous diversity
scenario, although the following remarks must be made: firstly, notice that the large size of
the cluster ensemble may not only impede flat consensus, but also the execution of those
RHCA variants using larger mini-ensembles (see the curves corresponding to the MCLA
consensus function). And secondly, it is noteworthy that the larger the cluster ensemble,
the greater running time savings –which can be as high as two orders of magnitude– are
derived from using the computationally optimal RHCA variant instead of flat consensus (in
the case it is executable), regardless of the consensus function employed.
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