The determination of the number of groups in a dataset, theircomposition and the most relevant measurements to be considered in clusteringthe data, is a high-demanding task, especially when the a priori information onthe dataset is limited. Three different genetic approaches are introduced in thispaper as tools for automatic data clustering and features selection. They differin the adopted codification of the grouping problem, not in the evolutionaryoperator and parameters. Two of them deals with the grouping problem in adeterministic framework. The first directly approaches the grouping problem asa combinatorial one. The second tries to determine some relevant points in thedata domain to be used in clustering data as group separators. A probabilisticframework is then introduced with the third one, which starts specifying thestatistical model from which data are assumed to be drawn. The evolutionaryapproaches are, finally, compared with respect to classical partitional clusteringalgorithms on simulated data and on Fisher’s Iris dataset used as a benchmark.

Evolutionary Approaches for Cluster Analysis / Minerva, Tommaso; Paterlini, Sandra. - In: SOFT COMPUTING. - ISSN 1432-7643. - STAMPA. - 8:(2003), pp. 165-176.

Evolutionary Approaches for Cluster Analysis

MINERVA, Tommaso;PATERLINI, Sandra
2003

Abstract

The determination of the number of groups in a dataset, theircomposition and the most relevant measurements to be considered in clusteringthe data, is a high-demanding task, especially when the a priori information onthe dataset is limited. Three different genetic approaches are introduced in thispaper as tools for automatic data clustering and features selection. They differin the adopted codification of the grouping problem, not in the evolutionaryoperator and parameters. Two of them deals with the grouping problem in adeterministic framework. The first directly approaches the grouping problem asa combinatorial one. The second tries to determine some relevant points in thedata domain to be used in clustering data as group separators. A probabilisticframework is then introduced with the third one, which starts specifying thestatistical model from which data are assumed to be drawn. The evolutionaryapproaches are, finally, compared with respect to classical partitional clusteringalgorithms on simulated data and on Fisher’s Iris dataset used as a benchmark.
8
165
176
Minerva, Tommaso; Paterlini, Sandra
Evolutionary Approaches for Cluster Analysis / Minerva, Tommaso; Paterlini, Sandra. - In: SOFT COMPUTING. - ISSN 1432-7643. - STAMPA. - 8:(2003), pp. 165-176.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/640897
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