TESI DOCTORAL - La Salle

D.1. Experiments on consensus-based self-refining
or KMSAD to the poorer qualities for EAC, HGPA or SLSAD), the self-refining process
manages to yield better clusterings in most cases, although the observed φ (NMI) increases
are, in general, modest.
Notice that, unfortunately, the supraconsensus function is reasonably successful in selecting
the top quality consensus clustering solution blindly.
D.1.11 PenDigits data set
Figure D.11 depicts the φ (NMI) values of the non-refined and self-refined consensus clusterings
resulting from the application of the consensus-based self-refining procedure on the
PenDigits data set. Remember that, on this collection, only the HGPA and MCLA consensus
functions are applicable using the hierarchical consensus architectures. Whereas the
quality of the clusterings obtained using HGPA is dramatically bad, the results obtained
with MCLA are pretty encouraging. The large φ (NMI) gain observed when refining the consensus
clustering λc output by RHCA is noteworthy. Moreover, notice that supraconsensus
selects correctly the highest quality clustering solution in this case.
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