A genetic algorithm for cluster analysis
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 he...
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| Veröffentlicht in: | Intelligent data analysis 2003, Vol.7 (1), p.15-25 |
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| container_title | Intelligent data analysis |
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| creator | Hruschka, Eduardo R. Ebecken, Nelson F.F. |
| description | 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. |
| doi_str_mv | 10.3233/IDA-2003-7103 |
| format | Article |
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| title | A genetic algorithm for cluster analysis |
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