Abstract
The determination of the number of groups in a dataset, their composition and the most relevant measurements to be considered in clustering the data, is a high-demanding task, especially when the a priori information on the dataset is limited. Three different genetic approaches are introduced in this paper as tools for automatic data clustering and features selection. They differ in the adopted codification of the grouping problem, not in the evolutionary operator and parameters. Two of them deals with the grouping problem in a deterministic framework. The first directly approaches the grouping problem as a combinatorial one. The second tries to determine some relevant points in the data domain to be used in clustering data as group separators. A probabilistic framework is then introduced with the third one, which starts specifying the statistical model from which data are assumed to be drawn. The evolutionary approaches are, finally, compared with respect to classical partitional clustering algorithms on simulated data and on Fisher’s Iris dataset used as a benchmark.
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Paterlini, S., Minerva, T. (2003). Evolutionary Approaches for Cluster Analysis. In: Bonarini, A., Masulli, F., Pasi, G. (eds) Soft Computing Applications. Advances in Soft Computing, vol 18. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1768-3_15
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DOI: https://doi.org/10.1007/978-3-7908-1768-3_15
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