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A genetic k-medoids clustering algorithm

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Abstract

We propose a hybrid genetic algorithm for k-medoids clustering. A novel heuristic operator is designed and integrated with the genetic algorithm to fine-tune the search. Further, variable length individuals that encode different number of medoids (clusters) are used for evolution with a modified Davies-Bouldin index as a measure of the fitness of the corresponding partitionings. As a result the proposed algorithm can efficiently evolve appropriate partitionings while making no a priori assumption about the number of clusters present in the datasets. In the experiments, we show the effectiveness of the proposed algorithm and compare it with other related clustering methods.

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Authors and Affiliations

  1. Department of Electronics, University of Kent, Canterbury, Kent, CT2 7NT, UK

    Weiguo Sheng

  2. Department of Information System and Computing, Brunel University, UB8 3PH, London, UK

    Xiaohui Liu

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  1. Weiguo Sheng
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  2. Xiaohui Liu
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Correspondence to Weiguo Sheng.

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Sheng, W., Liu, X. A genetic k-medoids clustering algorithm. J Heuristics 12, 447–466 (2006). https://doi.org/10.1007/s10732-006-7284-z

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  • DOI: https://doi.org/10.1007/s10732-006-7284-z

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