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5.3. Multimodal consensus clustering results
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Figure 5.9: φ (NMI) boxplots of the self-refined intermodal consensus clustering solutions
using the graph-cos-i2 algorithm on the IsoLetters data set.
Data set
CSPA EAC
Consensus function
HGPA MCLA ALSAD KMSAD SLSAD
IsoLetters 96.4 96.5 100 98.6 100 100 100
CAL500 89.3 92.9 99.3 97.8 67.9 83.6 82.1
InternetAds 75 57.1 46.4 98.5 78.6 90 85.7
Corel 100 89.2 100 100 100 100 100
Table 5.7: Percentage of multimodal self-refining experiments in which one of the selfrefined
consensus clustering solutions is better than its non-refined counterpart, across the
four multimedia data collections and the seven consensus functions.
periments conducted upon the cluster ensembles created using the |dfA| = 28 clustering
algorithms across the four multimedia data collections employed in this work.
For starters, in order to evaluate the ability of the self-refining process to create high
quality partitions, we have measured the percentage of experiments in which there exists
at least one self-refined consensus clustering λ pi
c that attains a higher φ (NMI) than its
non-refined counterpart λc. The results per data set and consensus function, which are
presented in table 5.7, reveal that self-refining is capable of yielding a beneficial effect in a
large majority (an average 90.2%) of the experiments conducted. This figure is of the same
order of magnitude of the one obtained in the consensus-based unimodal self-refining experiments
presented in section 4.2.1, which indicates that multimodality does not constitute
an obstacle as far as the performance of the proposed self-refining procedure is concerned.
Moreover, so as to evaluate the quality improvement that the proposed self-refining
procedure is able to introduce, we have computed the relative φ (NMI) gain between the
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