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论文类型：期刊论文

发表时间：2016-01-01

发表刊物：Journal of Computational and Theoretical Nanoscience

收录刊物：Scopus、EI

卷号：13

期号：1

页面范围：270-273

ISSN号：15461955

摘要：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.