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1

Dept. of Electronics and Telecommunication Engg, Jadavpur University, Kolkata, India

2

Norwegian University of Science and Technology, Norway

3

School of Computer Science and Engineering Chung-Ang University, Seoul, Korea





*

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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. View Full-Text

Keywords: differential evolution; multi-objective optimization; fuzzy clustering; micro-array data clustering differential evolution; multi-objective optimization; fuzzy clustering; micro-array data clustering





Autor: Kaushik Suresh 1, Debarati Kundu 1, Sayan Ghosh 1, Swagatam Das 1, Ajith Abraham 2 and Sang Yong Han 3,*

Fuente: http://mdpi.com/



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