TESI DOCTORAL - La Salle

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ALSAD rb−cos−i2
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Chapter 5. Multimedia clustering based on cluster ensembles
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EAC rb−cos−i2
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KMSAD rb−cos−i2
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HGPA rb−cos−i2
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SLSAD rb−cos−i2
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MCLA rb−cos−i2
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Figure 5.10: φ (NMI) boxplots of the self-refined intermodal consensus clustering solutions
using the rb-cos-i2 algorithm on the IsoLetters data set.
top quality self-refined consensus clustering and its non-refined counterpart, measured in
those experiments where there exists a self-refined consensus clustering superior to the nonrefined
version (i.e. a 90.2% of the total). Table 5.8 presents the results corresponding to
each data collection and consensus function. It can be observed that φ (NMI) gains over 10%
are consistently obtained in most cases —again, these results are of comparable magnitude
to those obtained in the unimodal scenario (see section 4.2.1). Notice that the results
obtained on the InternetAds data set stand out among the rest, as relative percentage
φ (NMI) gains of the order of 103 to 105 are observed. These extremely large figures are
due to the extremely low quality consensus clusterings available prior to refining on this
collection —thus transforming any φ (NMI) increase caused by self-refining into a huge figure
when expressed in relative percentage difference terms referred to the non-refined clustering.
In the 9.8% of the experiments in which none of the self-refined consensus clusterings (λ pi
c )
attains a higher quality than the non-refined one (λc), the difference between the top quality
λpi c and λc is an averaged relative percentage φ (NMI) loss of 19%.
Besides comparing the top quality self-refined consensus clustering solution with its nonrefined
counterpart, we have also contrasted its quality with respect to the clusterings that
make up the cluster ensemble.
Firstly, we have computed the percentage of cluster ensemble components that attain
ahigherφ (NMI) score (with respect to the ground truth) than that of the top quality selfrefined
consensus clustering, as this figure constitutes a pretty clear indicator of how does
it compare to the cluster ensemble it is created upon (see table 5.9). In average terms, the
top quality self-refined consensus clustering is better than the 78.3% of the cluster ensemble
components, which is a sign of notable robustness to the indeterminacies of multimodal
clustering. Moreover, recall that this percentage was 52.9% prior to self-refining, which
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