TESI DOCTORAL - La Salle

3.3. Deterministic hierarchical consensus architectures
SERT DHCA (sec.)
PERT DHCA (sec.)
10 1
10 0
|df A | = 10 , |df D | = 14 , |df R | = 5
ADR ARD DAR DRA RAD RDA flat
DHCA variant
10 0
(a) Serial estimated running time
|df A | = 10 , |df D | = 14 , |df R | = 5
ADR ARD DAR DRA RAD RDA flat
DHCA variant
(c) Parallel estimated running time
CSPA
EAC
HGPA
MCLA
ALSAD
KMSAD
SLSAD
CSPA
EAC
HGPA
MCLA
ALSAD
KMSAD
SLSAD
SRT DHCA (sec.)
PRT DHCA (sec.)
10 1
10 0
|df A | = 10 , |df D | = 14 , |df R | = 5
ADR ARD DAR DRA RAD RDA flat
DHCA variant
10 0
(b) Serial real running time
|df A | = 10 , |df D | = 14 , |df R | = 5
ADR ARD DAR DRA RAD RDA flat
DHCA variant
(d) Parallel real running time
CSPA
EAC
HGPA
MCLA
ALSAD
KMSAD
SLSAD
CSPA
EAC
HGPA
MCLA
ALSAD
KMSAD
SLSAD
Figure 3.11: Estimated and real running times of the serial and parallel DHCA on the Zoo
data collection in the diversity scenario corresponding to a cluster ensemble of size l = 570.
consensus architecture except when the EAC consensus function is employed —however,
this behaviour is not always successfully predicted by SERTDHCA, asdepictedinfigure
3.12(a). We believe this is due to the fact that our estimation is founded on the execution
time of a single consensus process.
As regards the parallel implementation of DHCA, the six DHCA variants yield very
similar real execution times, as shown in figure 3.12(d), a trend that the running time
estimation also captures —see figure 3.12(c). However, notice that this fact makes it difficult
that the absolute minima of PERTDHCA and PRTDHCA coincide, which will probably harm
the predictive accuracy of our proposal in the parallel implementation context. Moreover,
notice that when the MCLA consensus function is employed, flat consensus is not executable
(with the resources available in our experiments, see appendix A.6), while all the DHCA
variants are.
Diversity scenario |df A| =28
Figure 3.13 presents the estimated and real running times of the serial and parallel DHCA
implementations in the highest diversity scenario, i.e. the one corresponding to the creation
of the cluster ensemble by means of the |dfA| = 28 clustering algorithms of the CLUTO
clustering toolbox —which gives rise to a cluster ensemble containing l = 1596 components.
In this context, arranging the diversity factors in decreasing cardinality order for defining
their association to the DHCA stages again yields the most computationally efficient serial
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