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5.3. Multimodal consensus clustering results
φ (NMI)
1
0.8
0.6
0.4
0.2
0
image
λ rb−cos−i2
c
E
CSPA
EAC
HGPA
MCLA
ALSAD
KMSAD
SLSAD
(a) Modality 1
φ (NMI)
1
0.8
0.6
0.4
0.2
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speech
λ rb−cos−i2
c
E
CSPA
EAC
HGPA
MCLA
ALSAD
KMSAD
SLSAD
(b) Modality 2
φ (NMI)
1
0.8
0.6
0.4
0.2
0
image+speech
λ rb−cos−i2
c
E
CSPA
EAC
HGPA
MCLA
ALSAD
KMSAD
SLSAD
(c) Multimodal
φ (NMI)
1
0.8
0.6
0.4
0.2
0
λ c rb−cos−i2
E
CSPA
EAC
HGPA
MCLA
ALSAD
KMSAD
SLSAD
(d) Intermodal
Figure 5.6: φ (NMI) boxplots of the unimodal, multimodal and intermodal consensus clustering
solutions using the rb-cos-i2 algorithm on the IsoLetters data set.
clustering, it is necessary to conduct a more quantitative and generic analysis across the
experiments conducted on the |dfA| = 28 cluster ensembles created using all the clustering
algorithms from the CLUTO toolbox upon the four multimodal data collections employed
in this work.
Unimodal and multimodal consensus clustering vs their cluster ensembles
mod 1
Firstly, we have evaluated the quality of the two unimodal (λc mod 1+mod 2
multimodal (λc have been created upon.
mod 2
and λc )andthe
) consensus clusterings with respect to the cluster ensembles they
In order to evaluate how the consensus clusterings compare to their associated cluster
ensembles, we have computed the percentage of cluster ensemble components that attain
ahigherφ (NMI) than the evaluated consensus clustering. Quite obviously, the smaller this
percentage, the higher robustness to clustering indeterminacies is achieved. The results of
this analysis are presented in table 5.2.
Care must be taken in analyzing the presented percentages, due to the fact that the two
unimodal and the multimodal consensus clusterings have been created upon different cluster
ensembles, which makes comparisons across columns (i.e. across consensus functions) fair,
but the same does not hold for comparisons across rows (i.e. across consensus clusterings).
If the performance of the seven consensus functions is contrasted, it can be observed that
EAC and HGPA yield, in most cases, the worst results, as the consensus clusterings they
yield (either unimodal or multimodal) are, in average, worse than the 76.9% and 78.8% of
the components of the cluster ensemble they are created upon —a percentage that goes
below 30% in the case of the best performing consensus functions (CSPA, ALSAD and
KMSAD). These results confirm that there may exist great differences between consensus
functions as far as the quality of the consensus clusterings is concerned, so care must be
taken when choosing which ones are applied.
If averages are taken for summarization purposes, unimodal consensus clusterings are
better than the 49.2% of their corresponding cluster ensemble components, while this percentage
rises to 56.5% when multimodal consensus clusterings are considered.
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