TESI DOCTORAL - La Salle

List of Figures
3.11 Estimated and real running times of the serial and parallel dHCA implementations
on the Zoo data collection in the |dfA| = 10 diversity scenario . . . .
3.12 Estimated and real running times of the serial and parallel dHCA implemen-
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tations on the Zoo data collection in the |dfA| = 19 diversity scenario . . . .
3.13 Estimated and real running times of the serial and parallel dHCA implemen-
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tations on the Zoo data collection in the |dfA| = 28 diversity scenario . . . .
3.14 Evolution of the accuracy of DHCA running time estimation as a function of
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the number of consensus processes . . . . . . . . . . . . . . . . . . . . . . . 82
3.15 Running times of the computationally optimal RHCA, DHCA and flat consensus
architectures on the Zoo data collection for the diversity scenario
corresponding to a cluster ensemble of size l = 57 . . . . . . . . . . . . . . .
3.16 Running times of the computationally optimal RHCA, DHCA and flat consensus
architectures on the Zoo data collection for the diversity scenario
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corresponding to a cluster ensemble of size l = 570 . . . . . . . . . . . . . . 91
3.17 Running times of the computationally optimal RHCA, DHCA and flat consensus
architectures on the Zoo data collection for the diversity scenario
corresponding to a cluster ensemble of size l = 1083 . . . . . . . . . . . . . 92
3.18 Running times of the computationally optimal RHCA, DHCA and flat consensus
architectures on the Zoo data collection for the diversity scenario
corresponding to a cluster ensemble of size l = 1596 . . . . . . . . . . . . . 93
3.19 Running times of the computationally optimal serial RHCA, DHCA and flat
consensus architectures across all data collections for all the diversity scenarios 95
3.20 Running times of the computationally optimal parallel RHCA, DHCA and
flat consensus architectures across all data collections for all the diversity
scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.21 φ
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(NMI) of the consensus solutions yielded by the computationally optimal
RHCA, DHCA and flat consensus architectures on the Zoo data collection
for the diversity scenario corresponding to a cluster ensemble of size l =57
3.22 φ
99
(NMI) of the consensus solutions yielded by the computationally optimal
RHCA, DHCA and flat consensus architectures on the Zoo data collection
for the diversity scenario corresponding to a cluster ensemble of size l = 570
3.23 φ
100
(NMI) of the consensus solutions yielded by the computationally optimal
RHCA, DHCA and flat consensus architectures on the Zoo data collection
for the diversity scenario corresponding to a cluster ensemble of size l = 1083 100
3.24 φ (NMI) of the consensus solutions yielded by the computationally optimal
RHCA, DHCA and flat consensus architectures on the Zoo data collection
for the diversity scenario corresponding to a cluster ensemble of size l = 1596 101
3.25 φ (NMI) of the consensus solutions obtained by the computationally optimal
parallel RHCA, DHCA and flat consensus architectures across all data collections
for all the diversity scenarios . . . . . . . . . . . . . . . . . . . . . . 103
4.1 φ (NMI) boxplots of the self-refined consensus clustering solutions on the Zoo
data collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
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