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Indexed by:期刊论文

Date of Publication:2016-01-01

Journal:Journal of Computational and Theoretical Nanoscience

Included Journals:EI、Scopus

Volume:13

Issue:1

Page Number:270-273

ISSN No.:15461955

Abstract:Fuzzy clustering is an important problem, which is the subject of active research in several realworld applications. Fuzzy c-means (FCM) algorithm is one of the most popular fuzzy clustering techniques because it is efficient, straightforward, and easy to implement. However, FCM is sensitive to initialization and is easily trapped in local optima. Particle swarm optimization (PSO) is a stochastic global optimization tool which is used in many optimization problems. In this paper, a hybrid fuzzy clustering method based on FCM and fuzzy adaptive particle swarm optimization (FAPSO) is proposed which makes use of the merits of both algorithms. Experimental results show that our proposed method is efficient and can reveal encouraging results. ? 2016 American Scientific Publishers All rights reserved.