计算机工程与应用Issue(22)：115-118,122,5.DOI:10.3778/j.issn.1002-8331.1304-0211
改进的粒子群模糊聚类算法
Improved fuzzy clustering algorithm based on particle swarm optimization
摘要
Abstract
Aiming at the problem of traditional fuzzy C-means clustering algorithm that it is sensitive to the initial clustering centers and easy to fall into the local optimization, an improved algorithm that combines Particle Swarm Optimization algorithm with FCM algorithm is proposed. Depending on utilizing the global searching ability of Particle Swarm Optimization algorithm instead of the FCM algorithm, the new algorithm searches the initial cluster centers and escapes from the local optimization so as to achieve fuzzy clustering at last. Meanwhile, it mainly redesigns the fitness function from the perspective of compactness in intra-class and separation in inter-class. The experimental results show that the proposed algorithm has a better effect on both the cluster validity indexes and clustering accuracy.关键词
模糊聚类/模糊C-均值聚类算法/粒子群优化算法/紧凑性/分离性Key words
fuzzy clustering/Fuzzy C-means(FCM)/Particle Swarm Optimization(PSO)/compactness/separation分类
信息技术与安全科学引用本文复制引用
钱雪忠,李静,宋威..改进的粒子群模糊聚类算法[J].计算机工程与应用,2013,(22):115-118,122,5.基金项目
国家自然科学基金（No.61103129，No.61202312）；江苏省科技支撑计划资助项目（No.BE2009009）。 （）
