TESI DOCTORAL - La Salle

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Consensus
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Chapter 5. Multimedia clustering based on cluster ensembles
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Figure 5.2: Deterministic hierarchical consensus architecture DRM variant operating on
a cluster ensemble created using three diversity factors: three dimensionality |dfD| =3of
three object representations |dfR| = 3 and three modalities |dfM| = 3. The cluster ensemble
component obtained upon the jth object representation with the ith dimensionality on the
kth modality is denoted as λi,j,k. Consensus are sequentially created across the dimension,
representation and modality diversity factors (dfD, dfR and dfM, respectively).
ii) In section 5.3.2, we analyze the quality of the self-refined intermodal consensus
clusterings λc p i obtained upon select cluster ensembles containing a percentage
pi of the partitions of the original multimodal cluster ensemble E. Moreover,
we also evaluate the performance of the supraconsensus function as a means
for selecting, in a fully unsupervised manner, the top quality (either refined or
non-refined) consensus clustering.
– How do we measure it?
i) The quality of the unimodal, multimodal and intermodal consensus clusterings
obtained is evaluated in terms of their φ (NMI) with respect to the ground truth of
the data set. Inter-consensus clusterings comparisons are conducted in terms of
their relative percentage φ (NMI) differences, and the percentage of experiments in
which one of them attains higher φ (NMI) scores than the rest. Moreover, comparisons
between the consensus clusterings and their associated cluster ensembles
are made in terms of relative percentage φ (NMI) differences, the percentage of
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