TESI DOCTORAL - La Salle

Chapter 5. Multimedia clustering based on cluster ensembles
Moreover, the consensus clustering solution deemed as the optimal one (across
a majority of experiment runs) by the supraconsensus function is identified by
means of a vertical green dashed line. Moreover, the quality comparisons between
the self-refined consensus clusterings, the non-refined consensus clusterings and
the cluster ensemble components are presented by means of tables showing the
average values of the measured magnitudes on each one of the four multimodal
data collections employed in this work.
– Which data sets are employed? Only the multimodal consensus clustering results
obtained on the IsoLetters data collection are described in detail in this section —a
thorough portrayal of the experiments corresponding to the three other multimedia
data collections employed in this work (CAL500, InternetAds and Corel) can be found
in appendix E. However, the global evaluation of our proposals encompasses the
results obtained on the four multimodal data collections, presenting the average values
as a result.
5.3.1 Consensus clustering per modality and across modalities
This section is devoted to the evaluation of the intermediate (i.e. unimodal and multimodal)
and final (that is, intermodal) consensus clustering solutions yielded by the proposed multimodal
deterministic hierarchical consensus architecture applied on the IsoLetters data set.
Recall that the objects contained in this data collection are instances of the letters of the
English alphabet expressed in two original modalities, speech and image.
We start with a visual evaluation of the quality of the aforementioned consensus clusterings,
measured in terms of their normalized mutual information φ (NMI) with respect to
the ground truth of the data set (the closer its value to unity the higher the quality of the
corresponding clustering). Moreover, we also constrast them with the components of the
multimodal cluster ensemble they are created upon.
For starters, figure 5.3 depicts four boxplot charts corresponding to the φ (NMI) scores of
the cluster ensemble components and the unimodal, multimodal and intermodal consensus
clusterings, in the case the cluster ensemble compiles partitions output by the agglo-cosupgma
clustering algorithm. In each one of the boxplots, the φ (NMI) values of the components
of the cluster ensemble E and of the consensus clusterings yielded by each one of the
seven consensus functions employed in this work across ten experiment runs are shown. In
particular, figures from 5.3(a) to 5.3(c) depict the quality of the intermediate unimodal and
multimodal consensus clustering solutions λimage c , λspeech c ,andλimage+speech c , respectively.
Last, figure 5.3(d) shows the boxplots corresponding to the intermodal consensus clustering
λc, resulting from the combination of the previous three.
There are several observations worth making in view of these results. Firstly, it is to note
that pretty diverse quality consensus clusterings are obtained depending on the consensus
function employed. Clearly, EAC and HGPA yield the worst results, whereas the five other
consensus functions tend to yield better consensus partitions, being often able to compete
with the highest quality cluster ensemble components.
Secondly, focusing on the unimodal consensus clustering processes solely (figures 5.3(a)
and 5.3(b)), notice the substantial differences between the φ (NMI) values of the cluster ensemble
components corresponding to the image and speech modalities. Undoubtedly, this
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