One of the most challenging problems in data clustering is to determine the number of clusters. This study intends to propose a two-stage automatic clustering based differential evolution (DE) algorithm. Basically, integration of the cluster defragmented algorithm (CDA) and improved automatic clustering based differential evolution (ACDE) and k-means algorithm (CDA-ACDE-k-means) is proposed. In the first stage, CDA algorithm is used to determine k initial cluster number, while ACDE algorithm and k-means algorithm are performed for cluster analysis in the second stage. It requires no prior knowledge about number of clusters. The 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, four well-known data sets, Iris, Wine, Glass and Vowel, are employed. The computational results indicate that the proposed CDA-ACDE-k-means algorithm is superior to classical DE algorithm (CDE) and automatic clustering based improved differential evolution and k-means (ACDE-k-means), also other state of the art clustering algorithms based on genetic algorithm (GA) and particle swarm optimization (PSO) algorithm.

One of the most challenging problems in data clustering is to determine the number of clusters. This study intends to propose a two-stage automatic clustering based differential evolution (DE) algorithm. Basically, integration of the cluster defragmented algorithm (CDA) and improved automatic clustering based differential evolution (ACDE) and k-means algorithm (CDA-ACDE-k-means) is proposed. In the first stage, CDA algorithm is used to determine k initial cluster number, while ACDE algorithm and k-means algorithm are performed for cluster analysis in the second stage. It requires no prior knowledge about number of clusters. The 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, four well-known data sets, Iris, Wine, Glass and Vowel, are employed. The computational results indicate that the proposed CDA-ACDE-k-means algorithm is superior to classical DE algorithm (CDE) and automatic clustering based improved differential evolution and k-means (ACDE-k-means), also other state of the art clustering algorithms based on genetic algorithm (GA) and particle swarm optimization (PSO) algorithm.

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