TESI DOCTORAL - La Salle

SERT DHCA (sec.)
PERT DHCA (sec.)
10 1
10 0
|df A | = 19 , |df D | = 14 , |df R | = 5
ADR ARD DAR DRA RAD RDA flat
DHCA variant
10 0
(a) Serial estimated running time
|df A | = 19 , |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
Chapter 3. Hierarchical consensus architectures
SRT DHCA (sec.)
PRT DHCA (sec.)
10 1
10 0
|df A | = 19 , |df D | = 14 , |df R | = 5
ADR ARD DAR DRA RAD RDA flat
DHCA variant
10 0
(b) Serial real running time
|df A | = 19 , |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 3.12: 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 = 1083.
DHCA variant (ADR in this case). This fact somehow reinforces the idea that, when
compared to the typically linear or quadratic time complexity of consensus functions, the
multiplicative growth rate of the total number of consensus imposes a stronger constraint
as far as the running time of the DHCA is concerned. As observed in the previous diversity
scenarios, the selection of a particular DHCA variant is a less critical matter when the fully
parallel implementation of DHCA is considered, as all six variants yield pretty similar real
execution times. In the serial case, in contrast, the accuracy of the running time estimation
methodology is much more crucial, although SERTDHCA performs as a reasonably successful
predictor.
As mentioned earlier, these same experiments have been run on the Iris, Wine, Glass,
Ionosphere, WDBC, Balance and MFeat unimodal data collections, and the corresponding
results are presented in appendix C.3. Most of the conclusions drawn regarding the computational
efficiency of hierarchical and flat consensus architectures in the analysis of random
hierarchical consensus architectures are also applicable in the context of DHCA, such as
the high computational efficiency of i) parallel DHCA even in low diversity scenarios, and
ii) serial DHCA in medium and high diversity scenarios, or the dependence between the
characteristics of the consensus function employed for conducting clustering combination
and the execution time of consensus architectures.
Moreover, two extra conclusions regarding the selection of the computationally optimal
DHCA variant must be discussed. Firstly, defining the DHCA architecture by means of an
ordered list of diversity factors arranged in decreasing cardinality order (i.e. associating the
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