TESI DOCTORAL - La Salle

6.4. Experiments
Data set Soft cluster ensemble size l
Zoo 285
Iris 45
Wine 225
Glass 145
Ionosphere 485
WDBC 565
Balance 35
Mfeat 30
miniNG 365
Segmentation 260
BBC 285
PenDigits 285
Table 6.1: Soft cluster ensemble sizes l corresponding to the unimodal data sets.
φ (NMI)
1
0.8
0.6
0.4
0.2
ZOO
0
0 1 2 3 4
CPU time (sec.)
CSPA
EAC
HGPA
MCLA
VMA
BC
CC
PC
SC
Figure 6.2: φ (NMI) vs CPU time mean ± 2-standard deviation regions of the soft consensus
functions on the Zoo data collection.
efficiency of VMA is quite expectable, due to the fact that it simultaneously solves the
cluster correspondence problem and voting following an iterative procedure (Dimitriadou,
Weingessel, and Hornik, 2002), whereas in SC, PC, BC and CC, these two processes are
sequentially conducted.
As regards the quality of the consensus clustering solutions, notice that the four consensus
functions proposed achieve almost identical φ (NMI) scores than the best performing
state-of-the-art alternative, VMA.
Table 6.2 presents the significance level values obtained from all the t-paired tests conducted
on the Zoo data set. The upper and lower triangular sections of the table correspond
to the comparison between consensus functions in terms of CPU time and φ (NMI) , respectively.
When pairwise comparisons between the ith and the jth consensus functions result
in statistically significant differences, the corresponding significance level value p is presented
in the (i,j)th entry of the table (or in the (j,i)th entry, depending on whether it is a
comparison in terms of CPU time or φ (NMI) ). Otherwise, the lack of statistically significant
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