TESI DOCTORAL - La Salle

1.5 Motivation and contributions of the thesis
Chapter 1. Framework of the thesis
The main motivation of this thesis is the construction of an efficient multimodal clustering
system that performs as autonomously as possible, avoiding the re-execution of the different
stages of the knowledge discovery process. As these feedback loops are caused by suboptimal
decision-making, our idea is setting cluster practitioners free from the obligation of making
such critical decisions in a blind way, obtaining, at the same time, clustering solutions which
are robust to the clustering indeterminacies presented in the previous section.
Instead of being forced to blindly select a single clustering configuration, the user is
encouraged to use and combine all the data modalities, representations and clustering algorithms
at hand, generating as many individual clustering solutions (compiled into a cluster
ensemble) as possible. It will be the proposed system which, in a fully unsupervised mode,
outputs a consensus clustering solution that will hopefully be comparable to (or even better
than) the one achieved using the best clustering configuration among the available ones.
As the informed reader may have guessed, the approach followed in the quest for this
goal localizes in the consensus clustering framework, which is defined as “the problem of
combining multiple partitionings of a set of objects into a single consolidated clustering
without accessing the features or algorithms that determined these partitionings” (Strehl
and Ghosh, 2002). That is, the data representations, modalities and clustering algorithms
employed for generating the individual partitions are not of the system’s concern, as it will
operate on the individual clustering solutions regardless of the way they were created.
However, applying consensus clustering on cluster ensembles as a means for obtaining
robust clustering solutions is not new—in fact it has been a central or collateral matter in
several works (Strehl and Ghosh, 2002; Fred and Jain, 2003; Sevillano et al., 2006a; Fern
and Lin, 2008). Anyway, this thesis deals with several crucial and, to our knowledge, little
addressed issues in this context, such as:
– the computational burden imposed by the use of large cluster ensembles generated by
crossing multiple data modalities, representations and clustering algorithms.
– the quality decrease of the consensus clustering solution caused by the wide diversity
of the cluster ensemble.
– the application of cluster ensembles on the multimodal clustering problem.
– the definition of methods for building consensus clustering solutions (either crisp or
fuzzy) from the outputs of soft clustering algorithms.
As a systematic response to these challenges, this thesis puts forward the following
proposals:
– parallelizable hierarchical consensus architectures for creating consensus clustering
solutions in a computationally efficient way (see chapter 3).
– fully unsupervised consensus self-refining procedures, so as to drive the quality of
the consensus clustering solution near or even above the best available individual
clustering configuration —see chapter 4.
25