TESI DOCTORAL - La Salle

7.3. Multimedia clustering based on cluster ensembles
cluster ensemble (a selection process that is given the name of supraconsensus (Strehl and
Ghosh, 2002)).
The analysis of the quality (measured in terms of normalized mutual information with
respect to the ground truth) of the set of self-refined consensus clusterings obtained at each
experiment reveals that the proposed self-refining procedure is notably successful, as it is
higher than that of the reference clustering in a 83% (for consensus based self-refining) or a
56% (for selection based self-refining) of the experiments conducted. Furthermore, we have
also observed that producing multiple self-refined consensus clusterings is a highly beneficial
approach, as the highest quality self-refined clustering is obtained for very disparate values
of p depending on the experiment —from p=2% to p=90%, thus it would be pretty easy to
select a suboptimal value of p if a single one was chosen. As far as the quality gains induced
by the self-refining procedure are concerned, relative percentage φ (NMI) increases (referred
to the non-refined consensus clustering) higher –and quite often much higher– than 10%
are obtained in a vast majority of the experiments conducted.
A further advantage of the self-refining procedure is its ability to uniformize the quality of
the consensus clustering solutions created by distinct consensus architectures –reducing the
variances between their φ (NMI) scores by a factor of 20–, thus making it easier to decide which
is the most appropriate consensus architecture for a given consensus clustering problem on
computational grounds solely.
However, the good performance of the proposed self-refining procedure is somewhat
tarnished by the limited accuracy of the supraconsensus selection process, which manages
to select the highest quality self-refined consensus clustering in less than the half of the
experiments conducted, which causes an average 14% relative φ (NMI) reduction between the
consensus clustering selected by supraconsensus and the top quality one.
For this reason, the main research activities in this area should be directed, in our
opinion, towards the derivation of accurate supraconsensus selection techniques capable of
choosing, in a fully blind manner and as precisely as possible, the highest quality consensus
clustering among a given bunch of them.
Last, we have pleasingly noticed that fighting the expectable quality decrease suffered
by consensus clusterings created upon large cluster ensembles has also drawn the interest
of other authors. Curiously enough, this issue has also been tackled in (Fern and Lin,
2008) in a very similar fashion to our selection based self-refining procedure, which can be
interpreted as a sign of the good sense of our proposals.
7.3 Multimedia clustering based on cluster ensembles
Undoubtedly, ‘going multimedia’ is a beneficial trend, as it provides a richer vision of
information. However, it poses a challenge when multimodal data is to be processed by
means of unsupervised learning techniques (e.g. clustering), as the existence of multiple
modalities increases the uncertainties about what is the best way to represent, classify or
describe the data. In this sense, intuition tends to suggest that constructive interactions
between the distinct modalities exist, which should lead to a better explanation of the
data. However, it is not clear how this modality fusion should be conducted, either at a
feature level (early fusion) or at a decision level (late fusion). Indeed, our experiments have
demonstrated that early fusion is not always advantageous as regards the quality of the
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