TESI DOCTORAL - La Salle

Chapter 7. Conclusions
ing upon the aforementioned cluster ensemble, which gives rise to the first two proposals
put forward in this thesis: hierarchical consensus architectures and consensus self-refining
procedures, which are reviewed in sections 7.1 and 7.2, respectively.
Our proposals for robust clustering based on cluster ensembles find a natural field of
application in multimedia data clustering, as the existence of multiple data modalities
poses additional indeterminacies that challenge the obtention of robust clustering results.
Moreover, our strategy naturally allows the simultaneous use of early and late multimodal
fusion techniques, which constitutes a highly generic approach to the problem of multimedia
clustering. In section 7.3, our proposals in this area are reviewed.
The last proposal of this thesis can be regarded as a first step for generalizing our cluster
ensembles based robust clustering approach, as it consists of several voting based consensus
functions for soft cluster ensembles —recall that crisp clustering is in fact a simplification of
its fuzzy counterpart. These consensus functions, which are reviewed in section 7.4, can also
be considered a response to the relatively few efforts devoted to the derivation of consensus
clustering strategies in the context of fuzzy clustering.
We have given great importance to the experimental evaluation of all our proposals. To
that effect, we have employed several state-of-the-art consensus functions for hard cluster
ensembles –hypergraph based (CSPA, HGPA, MCLA) (Strehl and Ghosh, 2002), evidence
accumulation (EAC) (Fred and Jain, 2005) and similarity-as-data based (ALSAD, KMSAD,
SLSAD) (Kuncheva, Hadjitodorov, and Todorova, 2006)– to implement our self-refining
hierarchical consensus architectures. Moreover, the fuzzy versions of CSPA, EAC, HGPA
and MCLA, plus the VMA soft consensus function (Dimitriadou, Weingessel, and Hornik,
2002) have been used as an evaluation benchmark for our voting based consensus functions
for soft cluster ensembles. Our proposals have been tested over a total of sixteen unimodal
and multimodal data collections, which contain a number of objects ranging from hundreds
to several thousands. In particular, the performance of self-refining hierarchical consensus
architectures has been evaluated on both unimodal (chapters 3 and 4) and multimodal data
collections (chapter 5), whereas the experiments concerning soft consensus functions have
been conducted on the 12 unimodal collections —see chapter 6. However, in the near future,
we plan extending these latter experiments towards multimodal data sets. We expect such
extension to be little costly, since any consensus function can easily accommodate our early
plus late fusion multimedia clustering proposal. In this sense, we also intend to apply
our multimedia clustering system on well-known multimodal data sets such as VideoClef
(composed of video data along with speech recognition transcripts, metadata and shot-level
keyframes) (VideoCLEF, accessed on May 2009) and ImageClef (still images annotated with
text) (ImageCLEF, accessed on May 2009).
Furthermore, we have conducted our experiments on cluster ensembles of very different
sizes (from 6 to 5124 clusterings), in order to evaluate the influence of this factor on the
computational facet of our proposals. In all the experiments, the statistical significance
of the results at the 5% significance level has been evaluated, either explicitly or by their
presentation by means of boxplot charts.
As mentioned in appendix A.6, the experiments conducted in this thesis have been
run under Matlab 7.0.4 on Dual Pentium 4 3GHz/1 GB RAM computers. A total of
20 computers were employed during approximately 9 months at an almost constant pace,
combining for an estimated total running time of more than 10 years!
These experimental variablilty has provided us with a comparative view of the state-
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