TESI DOCTORAL - La Salle

1.4. Clustering indeterminacies
Data set Best results Mode #1 Mode #2 Multimodal
CAL500
Corel
InternetAds
IsoLetters
φ (NMI) 0.411 (P100) 0.249 (P74) 0.310 (P88)
algorithm rbr-cos-i2 graph-cos-i2 graph-jacc-i2
representation RP, r=120 PCA, r=40 ICA, r=100
φ (NMI) 0.669 (P99) 0.270 (P40) 0.675 (P100)
algorithm rbr-corr-i2 rb-cos-e1 rbr-corr-i2
representation RP, r=300 NMF, r=400 NMF, r=550
φ (NMI) 0.430 (P100) 0.258 (P98) 0.319 (P99)
algorithm bagglo-cos-slink agglo-corr-clink graph-jacc-i2
representation RP, r=70 Baseline NMF, r=150
φ (NMI) 0.754 (P93) 0.537 (P67) 0.897 (P100)
algorithm rbr-corr-i2 graph-jacc-i2 rbr-corr-i2
representation Baseline ICA, r=12 PCA, r=100
Table 1.2: Illustration of the clustering indeterminacies on the CAL500, Corel, InternetAds
and IsoLetters multimoda data sets. Each column presents the top-performing clustering
configuration for each separate modality and for the multimodal data representation.
The twenty-eight clustering algorithms employed in this work (see table A.1 for a quick
reference) are run on i) each modality of the data set, and ii) on the multimodal representation
obtained by early feature fusion, as described in appendix A.3.2. In both cases,
clustering is run on the baseline and feature extraction based data representations (see
section A.3.1 of appendix A).
As a summary of the obtained results and an illustration of the clustering indeterminacies,
table 1.2 presents the highest quality clustering results obtained in each case (i.e.
when clustering is conducted on either mode –mode #1 and mode #2 columns– and on
the multimodal representation), indicating the corresponding value of φ (NMI) , its percentile
in the global φ (NMI) distribution obtained, and the top-performing clustering configuration
(i.e. algorithm plus data representation plus dimensionality of the reduced feature space r
when needed).
As expected, there is no predominant modality across all the data sets. For the CAL500
collection, the clustering results obtained on mode #1 (text) are clearly superior to the rest.
A similar behaviour is observed in the InternetAds data set, where it is also mode #1 (images
size and aspect ratio, plus caption and alternate text in this case) the one that yields the
best clustering results, although its predominance is not as clear as in CAL500. In contrast,
the highest quality clustering solutions are obtained from multimodal representations in the
Corel and IsoLetters data sets.
Last but not least, notice the effect of the data representation and clustering algorithm
indeterminacies on all the data sets. Again, there is no universally superior clustering
algorithm nor data representation that guarantees the best clustering results. For a more
detailed description of the experimental results regarding the clustering indeterminacies in
multimodal data collections, see appendix B.2.
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