计算机工程与应用2009，Vol.45Issue(21)：97-98,188,3.DOI:10.3778/j.issn.1002-8331.2009.21.028
改进的模糊C均值聚类算法
Improved fuzzy C-means clustering algorithm
摘要
Abstract
Serf-adaptive strategy with the traditional fuzzy C-means clustering algorithm forms a new fuzzy clustering algorithm. Without prejudice to the speed of convergence,it can resolve the problems of local optimal and sensitivity to initial values.With the two data sets in the database of UCI machine learning for the study,the experimental results indicate that it does not lose the precision to the adaptive immune clustering algorithm.The number of clusters is accurate and its faster convergence is more important in the nowadays of high-spoed network data changing.关键词
模糊C均值聚类/自适应/簇的调整Key words
fuzzy C-means clustering/self-adaptive/chster adjustment分类
信息技术与安全科学引用本文复制引用
刘坤朋,罗可..改进的模糊C均值聚类算法[J].计算机工程与应用,2009,45(21):97-98,188,3.基金项目
国家自然科学基金(the National Natural Science Foundation of China under Grant No.60474070,No.10471036) （the National Natural Science Foundation of China under Grant No.60474070,No.10471036）
湖南省科技计划项目(No.05FJ3074) （No.05FJ3074）
湖南省教育厅重点项目(No.07A001). （No.07A001）
