初始化
模糊逻辑
模糊聚类
计算机科学
聚类分析
粒子群优化
数学优化
人工智能
数据挖掘
模糊集运算
机器学习
模糊集
数学
程序设计语言
作者
Hesam Izakian,Ajith Abraham
标识
DOI:10.1016/j.eswa.2010.07.112
摘要
Fuzzy clustering is an important problem which is the subject of active research in several real-world 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 PSO (FPSO) is proposed which make use of the merits of both algorithms. Experimental results show that our proposed method is efficient and can reveal encouraging results.
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