计算机工程与科学2017，Vol.39Issue(11)：2153-2160,8.DOI:10.3969/j.issn.1007-130X.2017.11.026
广义多变量模糊C均值聚类算法
A general multivariable fuzzy C-means clustering algorithm
摘要
Abstract
That the fuzzy index m must be larger than I can guarantee the convergence of the fuzzy clustering algorithm,however,it also restricts the universality of the clustering algorithm.We propose a novel clustering algorithm called the general multivariable fuzzy C-means clustering (GMFCM).Based on multivariable fuzzy C-means clustering (MFCM),the particle swarm optimization algorithm (PSO) is used to perform the optimization estimation on the fuzzy memberships of the GMFCM,thus the scope of the fuzzy index m is extended to m＞0,and the iterative formula of clustering center is derived by the gradient method for the GMFCM.We prove the thereom of new m value scope theoretically and discuss the convergence of the GMFCM.The GMFCM removes the restriction of the fuzzy clsutering on m,and makes up the incompleteness of the MFCM algorithm when the clustering center components and the sample components overlap.Simulation experiments prove the effectiveness of the GMFCM.关键词
模糊聚类/模糊指标/多变量模糊C均值聚类/粒子群优化算法/模糊隶属度Key words
fuzzy clustering/fuzzy index/multivariable fuzzy C-means clustering (MFCM)/particle swarm optimization(PSO)/fuzzy membership分类
信息技术与安全科学引用本文复制引用
文传军,汪庆淼..广义多变量模糊C均值聚类算法[J].计算机工程与科学,2017,39(11):2153-2160,8.基金项目
国家自然科学基金(61170126) （61170126）
常州工学院校级课题(YN1305) （YN1305）
