TESI DOCTORAL - La Salle

Abstract
When facing the task of partitioning a data collection in an unsupervised fashion, the clustering
practitioner must make several crucial decisions –which clustering algorithm to apply,
how the objects in the data set are represented, how many clusters are to be found, among
others– that condition, to a large extent, the quality of the resulting partition. However,
the unsupervised nature of the clustering problem makes it difficult (if not impossible) to
make well-founded decisions unless domain knowledge is available.
In an attempt to fight these indeterminacies, we propose an approach to the clustering
problem that intentionally reduces user decision making as much as possible. Rather the
contrary, the clustering practitioner is encouraged to simultaneously employ as many clustering
systems as possible (compiling their outcomes into a cluster ensemble), combining
them in order to obtain the final partition (or consensus clustering). The greater the similarity
between the highest quality cluster ensemble component and the consensus clustering,
the larger degree of robustness to the inherent indeterminacies of clustering is achieved.
However, the indiscriminate creation of cluster ensemble components poses two main
challenges to the clustering combination process, namely i) an increase of its computational
complexity, to the point that the creation of the consensus clustering can even become unfeasible
if the number of clustering systems combined is too large, and ii) the obtention of a
low quality consensus partition due to the inclusion of poor clustering systems in the cluster
ensemble. In order to fight against these inconveniences, this thesis introduces hierarchical
self-refining consensus architectures as a means for obtaining good quality partitions at a
reduced computational cost, as confirmed by extensive experimental evaluation.
Aiming to port this robust clustering strategy to a more generic framework, a set of
voting based consensus functions for fuzzy clustering systems combination is proposed.
Several experiments demonstrate that the quality of the consensus clusterings they yield is
comparable or better than that of multiple state-of-the-art soft consensus functions.
Our proposals find a natural field of application in the robust clustering of multimodal
data –a problem of current interest due to the growing ubiquity of multimedia–, as the
existence of multiple data modalities poses additional indeterminacies that challenge the
obtention of robust clustering results. The basis of our proposal is the creation of multimodal
cluster ensembles, which naturally allows the simultaneous use of early and late modality
fusion techniques, thus providing a highly generic and efficient approach to multimedia
clustering —the performance of which is analyzed in multiple experiments.
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