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
86.6 55.6 63.9 81.1 100.3 95.2 78.8
(76.8) (15.4) (2.4) (28.1) (65.3) (67.8) (34)
33.4 29.3 31.6 28.1 28.9 32.6 16
(18.9) (4.4) (7.6) (-) (-) (-) (-)
382.9 191.2 196.7 412 408.5 446.3 406.9
(314.2) (174.1) (–) (236.4) (222.7) (291.5) (356.6)
113.5 258 40.8 35.5 106.1 106 130.9
(83.8) (39.7) (2.6) (4.6) (58) (54.8) (225.5)
Table 5.13: Relative φ (NMI) percentage difference between the top quality (either non-refined
or self-refined) consensus clustering solution with respect to the median ensemble component
(or MEC), 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.
Data set
CSPA EAC
Consensus function
HGPA MCLA ALSAD KMSAD SLSAD
IsoLetters 53.6 67.9 70 50.7 39.3 42.9 39.3
CAL500 64.3 35.7 17.9 10.7 21.4 46.4 21.5
InternetAds 7.1 53.6 81.4 10 21.4 12.1 35.7
Corel 57.1 57.2 70 57.1 39.2 45 50
Table 5.14: Percentage of experiments in which the supraconsensus function selects the top
quality consensus clustering solution, across the four multimedia data collections and the
seven consensus functions.
system, and for this reason, the following paragraphs are devoted to its evaluation.
Evaluation of the supraconsensus process
Firstly, we have evaluated the supraconsensus function in terms of the percentage of experiments
in which it suceeds, i.e. it selects the highest quality consensus clustering solution
as the final partition λ final
c . The results obtained for each data set and consensus function
are presented in table 5.14, performing correctly in an average 42.1% of the experiments
conducted —that is, it is able to select the best clustering available in less than the half of
the occasions, which reveals (as outlined in chapter 4) that there is still room for improving
the performance of supraconsensus functions.
And secondly, we have analyzed how the consensus clustering selected by supraconsen-
, compares to the components of the cluster ensemble it is created upon —taking
sus, λ final
c
again the cluster ensemble components of maximum and median φ (NMI) (respectively referred
to as best and median ensemble component, or BEC and MEC for short) as a reference.
Hence, we have measured the relative percentage φ (NMI) differences between the
consensus clustering selected by supraconsensus and these cluster ensemble components, so
as to provide the reader with a notion of the impact of the apparent lack of accuracy of the
φ (ANMI) -based supraconsensus function.
The results, averaged across all the consensus functions, are presented in table 5.15.
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