TESI DOCTORAL - La Salle

Chapter 7
Conclusions
The contributions put forward in this thesis constitute a unitary proposal for robust clustering
based on cluster ensembles, with a specific focus on the increasingly interesting application
of multimedia data clustering and a view on its generalization in fuzzy clustering
scenarios. In this chapter, we summarize the main features of our proposals, highlighting
their strengths and weaknesses, and outlining some interesting directions for future research.
As for the robustness of clustering, recall that the unsupervised nature of this problem
makes it difficult (if not impossible) to select apriorithe clustering system configuration
that gives rise to the best 1 data partition. Furthermore, given the myriad of options –e.g.
clustering algorithms, data representations, etc.– available to the clustering practitioner,
such important decision making is marked by a high degree of uncertainty. As suboptimal
configuration decisions may give rise to little meaningful partitions of the data, it turns out
that these clustering indeterminacies end up being very relevant in practice, which, in our
opinion, justifies research efforts oriented to overcome them (such as the present one). This
was the main motivation of our first approaches to robust clustering via cluster ensembles
(Sevillano et al., 2006a; Sevillano et al., 2006b; Sevillano et al., 2007c), which have attracted
the attention of several researchers (Tjhi and Chen, 2007; Pinto, 2008; Gonzàlez and Turmo,
2008b; Tjhi and Chen, 2009).
For these reasons, our approach to robust clustering intentionally reduces user decision
making as much as possible, thus following an approach that is nearly opposite to the
procedure usually employed in clustering: instead of using a specific clustering configuration
(which is often selected blindly unless domain knowledge is available), the clustering
practitioner is encouraged to use and combine all the clustering configurations at hand,
compiling the resulting clusterings into a cluster ensemble, upon which a consensus clustering
is derived. The more similar this consensus clustering is to the highest quality clustering
contained in the cluster ensemble, the greater the robustness to clustering indeterminacies.
In this context, it must be noted that our particular approach to robust clustering foments
the creation of large cluster ensembles. This motivates that one of our main issues
of concern is the computationally efficient derivation of a high quality consolidated cluster-
1 The best data partition is an elusive concept in itself, as it basically depends on how the clustered
data is interpreted. However, for any given interpretation criterion, some clustering algorithms may obtain
better clusters than others (Jain, Murty, and Flynn, 1999). In this work, the quality of clusterings has been
evaluated by comparison with a allegedly correct cluster structure of the data, referred to as ground truth,
measuring their degree of resemblance by means of normalized mutual information, or φ (NMI) .
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