title: Multi-objective differential evolution for automatic clustering with application to micro-array data analysis.
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.
authors.affiliation: Dept. of Electronics and Telecommunication Engg, Jadavpur University, Kolkata, India; E-Mails:  kaushik_s1988@yahoo.com ;  kundu.debarati@gmail.com ;  sayan88tito@gmail.com ;  swagatamdas19@yahoo.co.in.
authors.forename: Kaushik
authors.initials: K
authors.lastname: Suresh
authors.forename: Debarati
authors.initials: D
authors.lastname: Kundu
authors.forename: Sayan
authors.initials: S
authors.lastname: Ghosh
authors.forename: Swagatam
authors.initials: S
authors.lastname: Das
authors.forename: Ajith
authors.initials: A
authors.lastname: Abraham
authors.forename: Sang Yong
authors.initials: SY
authors.lastname: Han