Titlebook: Metaheuristic Clustering; Swagatam Das,Ajith Abraham,Amit Konar Book 2009 Springer-Verlag Berlin Heidelberg 2009 algorithms.data mining.ev

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Differential Evolution Algorithm: Foundations and Perspectives,res of the classical Genetic Algorithms (GA). But it is much easier to implement than GA and applies a kind of differential mutation operator on parent chromosomes to generate the offspring. Since its inception in 1995, DE has drawn the attention of many researchers all over the world, resulting in

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Automatic Hard Clustering Using Improved Differential Evolution Algorithm,the existing clustering techniques, the proposed algorithm requires no prior knowledge of the data to be classified. Rather, it determines the optimal number of clusters in the data ‘on the run’. Superiority of the new method has been demonstrated by comparing it with two recently developed partitio

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Fuzzy Clustering in the Kernel-Induced Feature Space Using Differential Evolution Algorithm,curring groups in the data. The proposed method is based on a modified version of the classical Differential Evolution (DE) algorithm, which uses the neighborhood-based mutation strategy. It also employs a kernel-induced similarity measure instead of the conventional sum-of-squares distance. Use of

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