TESI DOCTORAL - La Salle

List of Figures
1.1 Evolution of the total number of websites across all Internet domains, from
November 1995 to February 2009 . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 Schematic diagram of the steps involved in a knowledge discovery process . 4
1.3 Taxonomy of data mining methods . . . . . . . . . . . . . . . . . . . . . . . 6
1.4 Toy example of a hierarchical clustering dendrogram . . . . . . . . . . . . . 10
1.5 Illustration of the data representation indeterminacy on the Wine and miniNG
data sets clustered by the rbr-corr-e1 algorithm. . . . . . . . . . . . . . 21
1.6 Block diagram of the robust multimodal clustering system based on selfrefining
hierarchical consensus architectures . . . . . . . . . . . . . . . . . . 26
2.1 Scatterplot of an artificially generated two-dimensional toy data set containing
n = 9 objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
2.2 Schematic representation of a consensus clustering process on a hard cluster
ensemble . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.1 Flat vs hierarchical construction of a consensus clustering solution on a hard
cluster ensemble . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.2 Three examples of topologies of random hierarchical consensus architectures 52
3.3 Evolution of RHCA parameters as a function of the mini-ensembles size b . 54
3.4 Estimated and real running times of the serial and parallel RHCA implementations
on the Zoo data collection in the |dfA| = 1 diversity scenario . . . . 58
3.5 Estimated and real running times of the serial and parallel RHCA implementations
on the Zoo data collection in the |dfA| = 10 diversity scenario . . . . 59
3.6 Estimated and real running times of the serial and parallel RHCA implementations
on the Zoo data collection in the |dfA| = 10 diversity scenario . . . . 61
3.7 Estimated and real running times of the serial and parallel RHCA implementations
on the Zoo data collection in the |dfA| = 28 diversity scenario . . . . 62
3.8 Evolution of the accuracy of RHCA running time estimation as a function of
the number of consensus processes . . . . . . . . . . . . . . . . . . . . . . . 65
3.9 An example of a deterministic hierarchical consensus architecture . . . . . . 71
3.10 Estimated and real running times of the serial and parallel dHCA implementations
on the Zoo data collection in the |dfA| = 1 diversity scenario . . . . 77
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