TESI DOCTORAL - La Salle

Chapter 3
Hierarchical consensus
architectures
As outlined in section 1.5, our proposal for building robust multimedia clustering systems
lies on the creation of consensus clustering solutions upon cluster ensembles. These ensembles
are made up of a large number of individual clusterings resulting from the execution of
multiple clustering algorithms on several unimodal and multimodal representations of the
objects contained in the data set.
Indeed, the massive crossing between clustering algorithms, object representations and
data modalities is a simple and parallelizable manner of generating highly diverse heterogeneous
cluster ensembles, entrusting the obtention of a meaningful combined clustering
solution to the consensus clustering task.
Given the unsupervised nature of the clustering problem, we think this is a pretty
sensible way of proceeding so as to obtain clustering solutions robust to the influence of the
clustering indeterminacies, as sticking to the use of a handful of clustering algorithms or
object representations can lead to an involuntary and undesirable limitation as regards the
quality and diversity of the cluster ensemble components.
However, at the same time that this strategy allows the creation of rich cluster ensembles,
it also introduces several drawbacks that affect the consensus clustering task:
– the large number of individual clustering solutions contained in the cluster ensemble,
resulting from the aforementioned combination of clustering algorithms, object representations
and data modalities, often leads to a notable increase in the computational
cost of the execution of the consensus function, which can even become prohibitive.
– this same fact incides in the diversity and quality of the cluster ensemble components,
and, while moderate diversity has been found to be beneficial as far as consensus
clustering is concerned (Hadjitodorov, Kuncheva, and Todorova, 2006; Fern and Lin,
2008), the existence of poor quality clustering solutions in the cluster ensemble may
cause a detrimental effect on the quality of the consensus clustering solution.
Allowing for these considerations, in this thesis we introduce the concept of self-refining
hierarchical consensus architectures (SHCA), defined as a generic means for fighting against:
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