TESI DOCTORAL - La Salle

Data set
IsoLetters
CAL500
InternetAds
Corel
Chapter 5. Multimedia clustering based on cluster ensembles
Consensus function
CSPA EAC HGPA MCLA ALSAD KMSAD SLSAD
46.4 3.6 0 26.4 64.3 51.4 25
(3.5) (0) (0) (0) (7.1) (5) (0)
7.1 0 0 0 3.6 2.9 3.5
(0) (0) (0) (0) (3.6) (2.9) (3.5)
3.6 0 0 0 0 1.4 0
(0) (0) (0) (0) (0) (0) (0)
75 0 0 45 92.9 72.9 10.7
(14.3) (0) (0) (0) (7.1) (0) (0)
Table 5.10: Percentage of experiments in which the best (either non-refined or self-refined)
consensus clustering solution is better than the best cluster ensemble component (or BEC),
across the four multimedia data collections and the seven consensus functions. The percentages
prior to self-refining are shown in brackets.
notice the aforementioned differences between the results offered by the distinct consensus
functions.
Data set
IsoLetters
CAL500
InternetAds
Corel
Consensus function
CSPA EAC HGPA MCLA ALSAD KMSAD SLSAD
4.2 11.2 – 2.4 8.2 6.9 8.3
(2.1) (–) (–) (–) (5.7) (3.2) (–)
5.6 – – – 10.4 3.2 3.3
(–) (–) (–) (–) (10.4) (3.2) (3.3)
6.2 – – – – 2.9 –
(–) (–) (–) (–) (–) (–) (–)
6.9 – – 2.1 6.1 5.4 13.7
(4.2) (–) (–) (–) (1.1) (–) (–)
Table 5.11: Relative φ (NMI) percentage difference between the top quality (either non-refined
or self-refined) consensus clustering solution with respect to the best ensemble component
(or BEC), across the four multimedia data collections and the seven consensus functions.
The relative φ (NMI) percentage differences prior to self-refining are shown in brackets.
Moreover, we have computed the relative percentage φ (NMI) gains between the top quality
(non-refined or refined) consensus clustering solution and the BEC –limited to those
experiments in which the former is superior to the latter–, obtaining the results presented
in table 5.11. If the φ (NMI) gains are averaged across all the experiments conducted, a 6.3%
relative percentage φ (NMI) increase is obtained. Again, the percentages corresponding to
the same magnitude measured prior to refining is presented in brackets in each box of the
table, attaining an average φ (NMI) gain of 4.1%. That is, self-refining does not only give rise
to a larger number of clusterings better than the BEC, but it also increases the φ (NMI) gains
with respect to it. However, in those experiments in which the top quality (non-refined or
refined) consensus clustering attains a φ (NMI) score which is lower than that of the BEC
(i.e. in a 80.9% of the total), its quality is a 28.2% lower, measured in averaged relative
percentage φ (NMI) terms.
As regards the comparison with the median quality cluster ensemble component (or
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