华中科技大学学报：自然科学版2012，Vol.40Issue(2)：107-111,132,6.
基于对应分析的冗余模糊C均值聚类算法研究
Study of multi-prototype fuzzy C-means clustering algorithm using correspondence analysis
摘要
Abstract
Novel fuzzy C-means （FCM） clustering algorithm based on inter-class separation and intra- class contraction was proposed, for the purpose of solving the problems that the FCM algorithm was sensitive to the initial prototypes, and it did not work well unless the shape of clusters was convex.Large clusters or elongated shaped clusters were first divided into lots of small clusters using weighted FCM. The elements of the fuzzy membership matrix were regarded as the features of small clusters, for they represented the degrees that samples belong to different classes. Correspondence analysis wasapplied to get the new features of small clusters, and small clusters were merged by using the weigh- ted FCM again to accomplish clustering. Experiment results on three typical datasets which can represent the clustering problems of curve segmentation and surface segmentation show that this method can well recognize irregular clusters, and validly avoid the dependency of the FCM on initial prototypes as well.关键词
模糊C均值聚类算法/对应分析/加权FCM算法/模糊隶属度矩阵/类间分离度/类内紧缩度Key words
fuzzy C-means clustering algorithm/correspondence analysis/weighted fuzzy C-meansclustering algorithm/fuzzy membership matrix/inter-class separation/intra-class con-traction分类
信息技术与安全科学引用本文复制引用
曾山,同小军,桑农,李蓉烨..基于对应分析的冗余模糊C均值聚类算法研究[J].华中科技大学学报：自然科学版,2012,40(2):107-111,132,6.基金项目
国家自然科学基金资助项目（61072143）. （）
