TESI DOCTORAL - La Salle

C.4. Computationally optimal RHCA, DHCA and flat consensus comparison
C.4.7 MFeat data set
This section describes the performance of the minimum complexity RHCA and DHCA
serial and parallel variants in the case of the MFeat data collection, which are compared to
classic flat consensus in terms of the time required for their execution and the quality of the
consensus clustering solutions they yield. Cluster ensembles of sizes l =6, 60, 114 and 168
correspond to the four diversity scenarios where these experiments are conducted.
Running time comparison
Figure C.47 presents the running times of flat consensus and of the serial implementations
of RHCA and DHCA. Notice that, except when the HGPA and MCLA consensus functions
are employed, flat consensus is faster than any of its hierarchical counterparts regardless of
the size of the cluster ensemble (i.e. it is faster in all the diversity scenarios).
When the parallel implementation of the HCA is considered (see figure C.48), the observed
behaviour is very similar to the one that has been just reported. That is, flat consensus
is the most computationally efficient consensus architecture, except when consensus
functions based on hypergraph partition are employed. This is due to the fact that, on the
MFeat data collection, the low cardinality of the diversity factors gives rise to relatively
small cluster ensembles, which makes flat consensus a competitive alternative to hierarhical
consensus architectures.
Consensus quality comparison
Figure C.49 presents the quality of the consensus clustering solutions yielded by the flat
and hierarhical consensus architectures, under the shape of φ (NMI) boxplot diagrams. An
inter-consensus function analysis reveals that EAC, HGPA and SLSAD yield, in general
terms, the lowest quality results, while CSPA, ALSAD and KMSAD stand out as the best
performing consensus functions, as they yield consensus clustering solutions the quality of
which is comparable to that of the cluster ensemble components that best reveal the true
cluster structure of the data set (i.e. those attaining the highest φ (NMI) values). Meanwhile,
if an intra-consensus function study is conducted, we can conclude that whereas the three
consensus architectures yield pretty similar quality consensus solutions when based on CSPA
and ALSAD, larger differences between RHCA, DHCA and flat consensus are observed in
other cases, as when consensus clustering is conducted by means of the EAC, HGPA, MCLA
or SLSAD consensus functions.
C.4.8 miniNG data set
In this section, we present the running times and quality evaluation (by means of φ (NMI)
values) of the consensus clustering processes implemented by means of the serial and parallel
RHCA and DHCA implementations and flat consensus on the miniNG data collection.
The cluster ensembles sizes corresponding to the four diversity scenarios in which our experiments
are conducted are l =73, 730, 1387 and 2044.
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