TESI DOCTORAL - La Salle

Chapter 5. Multimedia clustering based on cluster ensembles
when compared to their unimodal counterparts, constitute, in most cases, a better option.
Intermodal vs unimodal and multimodal consensus clustering
Furthermore, we have also investigated whether the combination of unimodal and multimodal
consensus clusterings (i.e the execution of intermodal consensus processes) can lead
to the obtention of better partitions.
For this reason, table 5.6 presents the detailed results corresponding to the comparison of
the intermodal consensus clustering solutions with respect to their unimodal and multimodal
counterparts, across all the data sets and consensus functions. Once again, such comparison
is twofold, as it takes into account the percentage of experiments in which intermodal
consensus is better than unimodal and multimodal, and the relative percentage φ (NMI)
differences between them (taking the unimodal and multimodal consensus clusterings as a
reference).
If averages across data collections and consensus functions are taken, the following results
are obtained: when compared to the unimodal consensus clusterings, intermodal consensus
are better than them in a 59.5% of the experiments conducted, attaining an average relative
φ (NMI) gain of 2821.7% with respect to them. That is, intermodal consensus clusterings are,
in general terms, superior to their unimodal counterparts. Possibly the clearest exceptions
to this rule are found in the audio modality of the CAL500 data set and the image mode
of the Corel collection.
However, intermodal consensus clusterings are superior to their multimodal counterparts
in just a 34.7% of the occasions, reaching a quality that, measured in average relative φ (NMI)
percentage terms, is a 65.5% better. Thus, as already suggested by the boxplots charts
presented in figures 5.3 to 5.6, intermodal consensus clusterings tend to become a trade-off
between the multimodal and unimodal consensus clustering solutions it is based on.
Furthermore, if the quality of the intermodal consensus clustering is contrasted to that of
the cluster ensemble it is created upon (that is, the one compiling both unimodal and multimodal
clusterings), we obtain that it is better than the 52.9% of its components —recall that
this percentage was 49.2% and 56.5% when referred to the unimodal and multimodal consensus
clusterings, which reinforces the notion that, in general terms, intermodal consensus
is a trade-off between its unimodal and multimodal counterparts.
Notice that pretty different situations are found among the data sets used in this experiment.
For instance, the intermodal consensus clustering is clearly inferior to its multimodal
conterpart on the IsoLetters data set, whereas quite the opposite is observed on the InternetAds
collection. Therefore, we consider that creating an intermodal consensus clustering
is a pretty generic way of proceeding, as sometimes it can be advantageous to combine unimodal
and multimodal consensus clusterings. Its eventual inferior quality (when compared
to either its unimodal and multimodal counterparts) can be compensated by the consensus
self-refining procedure presented in section 5.2. The results of applying it on the intermodal
consensus clustering λc are described in the following section.
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