Automatic Clustering Combining Differential Evolution Algorithm and k-Means Algorithm
One of the most challenging problems in data clustering is to determine the number of clusters. This study intends to propose an improved differential evolution algorithm which integrates automatic clustering based differential evolution (ACDE) algorithm and k-means (ACDE-k-means) algorithm. It requires no prior knowledge about number of clusters. k-means algorithm is employed to tune cluster centroids in order to improve the performance of DE algorithm. To validate the performance of the proposed algorithm, two well-known data sets, Iris and Wine, are employed. The computational results indicate that the proposed ACDE-k-means algorithm is superior to classical DE algorithm.
KeywordsAutomatic clustering Differential evolution k-means
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