A clustering algorithm using evolutionary programming

In this paper an evolutionary programming based clustering algorithm that effectively groups a given set of data into an optimal number of clusters is proposed. This proposed method is applicable for clustering task where clusters are crisp and spherical. This algorithm determines the optimum number of clusters and optimal cluster centers in such a way that locally optimal solutions are avoided. Another advantage is that the clustering here is independent of the initial choice of the cluster centers. We demonstrate with illustration that our method shows better performance than the k-means algorithm