TESI DOCTORAL - La Salle

Chapter 4. Self-refining consensus architectures
cluster ensembles of distinct sizes, and iv) the use of normalized mutual information as the
guiding principle for comparing clusterings.
However, there also exist several differences between both works, as in (Fern and Lin,
2008) i) refining is not presented as a means for bettering the quality of a previously
derived consensus clustering solution, but as a means for obtaining a good quality one
upon a select cluster ensemble resulting from discarding those poor components that may
induce a quality loss in it, ii) the criteria employed for choosing the components included
in the select cluster ensemble consider both clustering quality and diversity, iii) clustering
refinement results obtained by a single consensus function (CSPA) are reported, and iv) no
supraconsensus methodology for selecting the best refined consensus clustering is studied.
To conclude, we would like to highlight again the significant quality improvements that
can be obtained by means of self-refining consensus procedures. However, it is also important
to be aware that we cannot take full advantage of these gains if a good performing
supraconsensus methodology allows to select the top quality self-refined clustering solution
with a high level of confidence. For this reason, in our opinion, devising such a technique is
a matter of paramount importance as regards the further research to be conducted in this
particular field.
In the future, it would be interesting to analyze how consensus self-refining procedures
perform if the cluster ensemble selection process was based on clustering similarity measures
other than normalized mutual information. Furthermore, we also intend to study the
possibility of creating the select cluster ensemble by including in it all those clusterings
exceeding a certain φ (ANMI) threshold with respect to the reference clustering, instead of
selecting a percentage p of the cluster ensemble components.
4.5 Related publications
As mentioned earlier, the aim of the proposed self-refining consensus clustering approach
is to obtain partitions which are robust to the indeterminacies inherent to the clustering
problem. This has been the main propeller of our research since the early days, which
has been reflected in several publications at several international conferences and national
journals. The application focus of these works was document clustering, so they were
mainly published at Information Retrieval and Natural Language Processing forums. In
none of these works, however, self-refining procedures are included as a means for obtaining
improved quality clustering results, so our proposals in this specific area remain, by the
moment, unpublished.
The first publication regarding robust document clustering based on cluster ensembles
was presented as a poster at the SIGIR 2006 conference held at Seattle. The details of this
publication follow.
Authors: Xavier Sevillano, Germán Cobo, Francesc Alías and Joan Claudi Socoró
Title: Feature Diversity in Cluster Ensembles for Robust Document Clustering
In: Proceedings of the 29th ACM SIGIR Conference
Pages: 697-698
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