TESI DOCTORAL - La Salle

3.5. Discussion
Consensus Consensus function
architecture CSPA EAC HGPA MCLA ALSAD KMSAD SLSAD
flat 58.3 25 42.3 33.2 66.7 69.8 41.7
RHCA 69.8 24.7 15.9 74.2 79.1 79.1 50.4
DHCA 83.3 8.3 3.9 77.1 75 77.9 58.3
Table 3.12: Percentage of experiments in which the consensus clustering solution is better
than the median cluster ensemble component.
given ground truth (i.e. the BEC), we observe that the consensus clustering solution attains
higher φ (NMI) values in only a 0.1% of the experiments —see table 3.14. If the degree of
improvement of those consensus clustering solutions that attain a higher φ (NMI) than the
BEC is measured in terms of relative percentage φ (NMI) increase, a modest 0.6% φ (NMI)
gain is obtained in average —see table 3.15 for a detailed view across consensus functions
and architectures.
Consensus Consensus function
architecture CSPA EAC HGPA MCLA ALSAD KMSAD SLSAD
flat 90 12.9 80.2 50.1 107.1 87.7 24.6
RHCA 78.8 16.3 9.2 96.4 94.7 90.6 33.3
DHCA 73.7 11.6 5.6 53.1 83.7 72.2 67.6
Table 3.13: Relative percentage φ (NMI) gain between the consensus clustering solution and
the median cluster ensemble component.
As a conclusion, we can see that the application of consensus clustering processes on
a collection of partitions of a given data collection provides a means for obtaining a summarized
clustering that, although rarely better than the best component available in the
cluster ensemble, is reasonably often quite better than the median data partition. However,
despite these fairly good results, we aim to obtain clustering solutions more robust to the
inherent indeterminacies of clustering (i.e. closer or even better than the maximum quality
cluster ensemble component). For this reason, chapter 4 introduces what we call consensus
self-refining procedures that aim to improve the quality of the consensus clustering solutions
obtained from either hierarchical or flat consensus architectures.
3.5 Discussion
Our proposal for building clustering systems that behave robustly in front of the indeterminacies
inherent to unsupervised classification problems relies on the application of consensus
clustering processes on large cluster ensembles created by the application of multiple mutually
crossed diversity factors.
However, in the consensus clustering literature, relatively few works face the problematics
of combining large amounts of clusterings, as most authors tend to employ rather small
cluster ensembles for evaluating their proposals. However, the application of certain consensus
clustering approaches in computationally demanding scenarios can be difficult. Typical
examples of this include consensus functions based on object co-association measures that
become inapplicable on large data collections, or clustering combiners not executable on
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