Clustering Kmeans with Evolutionary Strategies
Kmeans algorithm is an unsupervised method of clustering, it is simple, fast and very facile to implemented, but it suffers from the initialization and local solution. Evolutionary strategies are particular methods for optimizing functions; they have a great ability to find the global optimum of a problem. In this paper, we propose a clustering method based on Kmeans algorithm, and evolutionary strategies noted evolutionary Kmeans EKM. The proposed approach is validated by the artificial dataset and well-known real data sets. We compared our approach with Kmeans, Fuzzy CMeans and Kmedoid. The results show the good performance of this approach.
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