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 Clustering Analysis / Minerva, T.; Paterlini, Sandra. - In: SOFT COMPUTING. - ISSN 1432-7643. - STAMPA. - 8:(2001), pp. 165-176.
Evolutionary Clustering Analysis
Minerva, T.;PATERLINI, Sandra
2001
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.Pubblicazioni consigliate
I metadati presenti in IRIS UNIMORE sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono rilasciati con licenza Attribuzione 4.0 Internazionale (CC BY 4.0), salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris