TESI DOCTORAL - La Salle

φ (NMI)
1
0.8
0.6
0.4
0.2
0
CSPA
E
RHCA
DHCA
flat
φ (NMI)
1
0.8
0.6
0.4
0.2
0
EAC
E
RHCA
DHCA
flat
φ (NMI)
1
0.8
0.6
0.4
0.2
0
HGPA
E
RHCA
DHCA
flat
φ (NMI)
Chapter 3. Hierarchical consensus architectures
1
0.8
0.6
0.4
0.2
0
MCLA
E
RHCA
DHCA
flat
φ (NMI)
1
0.8
0.6
0.4
0.2
0
ALSAD
E
RHCA
DHCA
flat
φ (NMI)
1
0.8
0.6
0.4
0.2
0
KMSAD
E
RHCA
DHCA
flat
φ (NMI)
1
0.8
0.6
0.4
0.2
0
SLSAD
E
RHCA
DHCA
flat
Figure 3.21: φ (NMI) of the consensus solutions yielded by the computationally optimal
RHCA, DHCA and flat consensus architectures on the Zoo data collection for the diversity
scenario corresponding to a cluster ensemble of size l = 57.
largest inter-consensus architecture deviations are found when consensus clustering is based
on the HGPA consensus function, as the notches of the corresponding φ (NMI) boxes are far
from overlapping. If the consensus functions are compared in terms of the quality of the
clustering solutions they yield, it can be observed that the best results are obtained by the
EAC, ALSAD, KMSAD and SLSAD consensus functions. In these cases, the medians of
the consensus solutions output by the consensus architectures are better than the 75% of
the components of the cluster ensemble E, which denotes a notable level of robustness to
the clustering indeterminacies.
Diversity scenario |df A| =10
Secondly, the quality of the consensus clustering solutions output by the consensus architectures
corresponding to the experiments conducted on the diversity scenario corresponding
to cluster ensembles of size l = 570 are presented in figure 3.22. The trends detected in the
previous diversity scenario are somehow confirmed in this case. That is, those consensus
functions based on evidence accumulation (EAC) and object similarity as data (ALSAD,
KMSAD and SLSAD) yield the best quality consensus clustering solutions, and they show
a high degree of independence with respect to the topology of the consensus architecture.
In fact, EAC and SLSAD based consensus architectures give rise to consensus clusterings
which are better than the 80% of the cluster ensemble components, which again reveals the
ability of these consensus functions to attain clustering solutions robust to the uncertainties
inherent to clustering. In contrast, consensus labelings obtained by hypergraph-based
consensus functions (CSPA, HGPA and MCLA) attain lower φ (NMI) values, while showing
a larger quality variabilty (this only applies to the HGPA and MCLA consensus functions).
Diversity scenario |df A| =19
The results corresponding to the experiments conducted in the third diversity scenario (i.e.
cluster ensembles generated by the compilation of the clusterings output by |dfA| =19
randomly selected clustering algorithms, giving rise to cluster ensembles of size l = 1083)
are presented in figure 3.23. The behaviour detected in the previous diversity scenarios
is also found in this case. Again, the largest inter-consensus architecture variations are
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