TESI DOCTORAL - La Salle

3.4. Flat vs. hierarchical consensus
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)
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 3.22: 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 = 570.
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)
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 3.23: 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 = 1083.
observed when consensus are built using the HGPA consensus function. In the remaining
cases, much smaller deviations are found (maybe with the exception of ALSAD, but the
observed dispersions are smaller than those of HGPA) —in fact, statistically non significant
differences between the three consensus architectures are observed in the EAC, MCLA,
KMSAD and SLSAD based architectures.
Diversity scenario |df A| =28
And last, the φ (NMI) values of the consensus clustering solutions yielded by the hierarchical
and flat consensus architectures corresponding to the experiments conducted on the
highest diversity scenario (i.e. cluster ensembles of size l = 1596) are presented in figure
3.24. This scenario is ideal for analyzing the variability of the quality of the consensus
clustering solutions output by the distinct consensus functions, as exactly the same cluster
ensemble has been employed in the ten experiments analyzed —in contrast, in the previous
diversity scenarios, the cluster ensemble employed in each one of the ten experiments was
created by compiling the clustering components generated by |dfA| = {1, 10, 19} randomly
picked clustering algorithms (that is, two superimposed randomness factors underlie the
boxplots presented in figures 3.21 to 3.23). In this sense, it can be observed that, for any
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