TESI DOCTORAL - La Salle

List of Tables
5.15 Relative φ (NMI) percentage differences between the best and median components
of the cluster ensemble and the consensus clustering λ final
c selected by
supraconsensus, across the four multimedia data collections . . . . . . . . . 160
6.1 Soft cluster ensemble sizes of the unimodal data sets . . . . . . . . . . . . . 186
6.2 Significance levels p corresponding to the pairwise comparison of soft consensus
functions using a t-paired test on the Zoo data set . . . . . . . . . . . . 187
6.3 Percentage of experiments in which the state-of-the-art consensus functions
(CSPA, EAC, HGPA, MCLA and VMA) yield better/equivalent/worse consensus
clustering solutions than the four proposed consensus functions (BC,
CC, PC and SC) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188
6.4 Percentage of experiments in which the state-of-the-art consensus functions
(CSPA, EAC, HGPA, MCLA and VMA) are executed faster/equivalent/slower
than the four proposed consensus functions (BC, CC, PC and SC) . . . . . 189
A.1 Cross-option table indicating the clustering strategy-criterion function-similarity
measure combinations available in CLUTO . . . . . . . . . . . . . . . . . . 220
A.2 Summary of the unimodal data sets employed in the experiments . . . . . . 222
A.3 Summary of the multimodal data sets employed in the experiments . . . . . 224
A.4 Cluster ensemble sizes corresponding to distinct algorithmic diversity configurations
for the unimodal data sets . . . . . . . . . . . . . . . . . . . . . . 228
A.5 Cluster ensemble sizes corresponding to distinct algorithmic diversity configurations
for the multimodal data sets . . . . . . . . . . . . . . . . . . . . . 229
B.1 Number of individual clusterings per data representation on each unimodal
data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234
B.2 Top clustering results of each clustering algorithm family sorted from highest
to lowest φ (NMI) on the unimodal collections . . . . . . . . . . . . . . . . . . 242
B.3 Number of individual clusterings per data representation on each multimodal
data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243
B.4 Top clustering results of each clustering algorithm family sorted from highest
to lowest φ (NMI) on the multimodal collections . . . . . . . . . . . . . . . . 248
C.1 Examples of computation of the number of stages s of a RHCA with l =
7, 8and9andb = 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 250
C.2 Examples of computation of the number of consensus per stage (Ki) ofa
RHCA with l =7, 8 and 9 and b = 2 . . . . . . . . . . . . . . . . . . . . . . 250
C.3 Examples of computation of the mini-ensembles size of a RHCA with l =
7, 8and9andb = 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251
C.4 Configuration of RHCA topologies on a cluster ensemble of size l =30with
varying mini-ensembles sizes . . . . . . . . . . . . . . . . . . . . . . . . . . . 251
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