Differential Evolution (DE) has emerged as one of the fast and efficient search heuristics of current interest. Combining DE and Fuzzy C-Means (DEFCM) explicitly improves the clustering on the basis of degree of membership. However, misdirection of the search, e.g., too much either exploitation or exploration search still ruin the achievement of global optimal solution. Thereby, this study proposes a DE-based fuzzy clustering using self-adaptive trade-off between exploitation and exploration (DEFSA). The efficiently dynamic trade-off is controlled by none of arbitrarily defined parameters. The performance measurements relate to F-measures, FCM objective degree and Xie-Beni validity index. The experiments are operated on real-world as well as artificial data sets. The results show the superior performance of the proposed method in terms of clustering correctness over traditional fuzzy ant-based clustering as well as some other efficient clustering methods.Differential Evolution (DE) has emerged as one of the fast and efficient search heuristics of current interest. Combining DE and Fuzzy C-Means (DEFCM) explicitly improves the clustering on the basis of degree of membership. However, misdirection of the search, e.g., too much either exploitation or exploration search still ruin the achievement of global optimal solution. Thereby, this study proposes a DE-based fuzzy clustering using self-adaptive trade-off between exploitation and exploration (DEFSA). The efficiently dynamic trade-off is controlled by none of arbitrarily defined parameters. The performance measurements relate to F-measures, FCM objective degree and Xie-Beni validity index. The experiments are operated on real-world as well as artificial data sets. The results show the superior performance of the proposed method in terms of clustering correctness over traditional fuzzy ant-based clustering as well as some other efficient clustering methods.
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