A hybrid heuristic for the k-medoids clustering problem
Proceedings of the 14th annual conference on Genetic and evolutionary computation
An evolutionary based clustering algorithm applied to dada compression for industrial systems
IDA'12 Proceedings of the 11th international conference on Advances in Intelligent Data Analysis
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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.