Abstract: This paper applies the Differential Evolution (DE) algorithm to the task of automatic fuzzy clustering in a Multi-objective Optimization (MO) framework. It compares the performances of two multi-objective variants of DE over the fuzzy clustering problem, where two conflicting fuzzy validity indices are simultaneously optimized. The resultant Pareto optimal set of solutions from each algorithm consists of a number of non-dominated solutions, from which the user can choose the most promising ones according to the problem specifications. A real-coded representation of the search variables, accommodating variable number of cluster centers, is used for DE. The performances of the multi-objective DE-variants have also been contrasted to that of two most well-known schemes of MO clustering, namely the Non Dominated Sorting Genetic Algorithm (NSGA II) and Multi-Objective Clustering with an unknown number of Clusters K (MOCK). Experimental results using six artificial and four real life datasets of varying range of complexities indicate that DE holds immense promise as a candidate algorithm for devising MO clustering schemes.
Keywords: differential evolution; multi-objective optimization; fuzzy clustering; micro-array data clustering
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Suresh, K.; Kundu, D.; Ghosh, S.; Das, S.; Abraham, A.; Han, S.Y. Multi-Objective Differential Evolution for Automatic Clustering with Application to Micro-Array Data Analysis. Sensors 2009, 9, 3981-4004.
Suresh K, Kundu D, Ghosh S, Das S, Abraham A, Han SY. Multi-Objective Differential Evolution for Automatic Clustering with Application to Micro-Array Data Analysis. Sensors. 2009; 9(5):3981-4004.
Suresh, Kaushik; Kundu, Debarati; Ghosh, Sayan; Das, Swagatam; Abraham, Ajith; Han, Sang Yong. 2009. "Multi-Objective Differential Evolution for Automatic Clustering with Application to Micro-Array Data Analysis." Sensors 9, no. 5: 3981-4004.