A genetic algorithm for cluster analysis

Article type: Research Article

Authors: Hruschka, Eduardo R. | Ebecken, Nelson F.F.

Affiliations: COPPE / Federal University of Rio de Janeiro, Brasil. E-mail: erh@onda.com.br, nelson@ntt.ufrj.br

Abstract: This paper describes a new approach to find the right clustering of a dataset. We have developed a genetic algorithm to perform this task. A simple encoding scheme that yields to constant-length chromosomes is used. The objective function maximizes both the homogeneity within each cluster and the heterogeneity among clusters. Besides, the clustering genetic algorithm also finds the right number of clusters according to the Average Silhouette Width criterion. We have also developed specific genetic operators that are context-sensitive. Four examples are presented to illustrate the efficacy of the proposed method.

Keywords: clustering, genetic algorithms, data mining

DOI: 10.3233/IDA-2003-7103

Journal: Intelligent Data Analysis, vol. 7, no. 1, pp. 15-25, 2003