计算机工程与应用2012，Vol.48Issue(35)：151-155,173,6.DOI:10.3778/j.issn.1002-8331.1204-0392
量子粒子群优化改进的模糊C均值聚类算法
Research of improved fuzzy C-means algorithm based on quantum-behavior particle swarm optimization
摘要
Abstract
Fuzzy C-Means(FCM) clustering algorithm has the shortcomings of being sensitive to the initial cluster centers and being trapped by local optima, To resolve two disadvantages, this paper proposes a novel clustering method using Quantum-behavior Particle Swarm Optimization (AQPSO) to optimize the improved FCM clustering algorithm (AF-AQ-AF), AQPSO algorithm is introduced based on a new metric standard which can lower the influence of initialized data points, quickly converge to the optimal solution and improve the global search ability. Data experimental results show the proposed algorithm avoids entering local minimum, enhances the convergence rate and gets a better result of clustering.关键词
聚类分析/模糊C-均值(FCM)/量子粒子群(QPSO)/新距离标准Key words
cluster analysis/ Fuzzy C-Means (FCM)/ Quantum-behavior Particle Swarm Optimization(QPSO)/new metric standard分类
信息技术与安全科学引用本文复制引用
李引,毛力,须文波..量子粒子群优化改进的模糊C均值聚类算法[J].计算机工程与应用,2012,48(35):151-155,173,6.基金项目
轻工过程先进控制教育部重点实验室开放课题资助(江南大学)项目(No.APCLI1004) （江南大学）
国家青年科学基金项目资助(No.F030204). （No.F030204）
