计算机工程与应用2012，Vol.48Issue(5)：198-200,3.DOI:10.3778/j.issn.1002-8331.2012.05.057
基于马氏距离特征加权的模糊聚类新算法
New fuzzy clustering algorithm based on feature weighted Mahalanobis distances
摘要
Abstract
Fuzzy clustering analysis is an important research field of the fuzzy pattern recognition, and the Fuzzy C-Means algorithm (FCM) is the most classical algorithm. It regards the sample features have the same contribution to the cluster result; not thinking the different features may have different impacts on the cluster result. When FCM processes some datasets of high correlation, error probability will be increased. Focusing on above two problems, this paper proposes an improved new fuzzy clustering algorithm based on feature weighted Mahalanobis distance function. Using adaptive Mahaianobis distance to weight the feature, the new algorithm can effectively cluster to the datasets of high correlation. Experiment illustrates its effectiveness and feasibility.关键词
模糊C均值/马氏距离/属性相关/特征加权Key words
Fuzzy C-Means/ Mahalanobis distances/ correlation of attributes/ feature weighted分类
信息技术与安全科学引用本文复制引用
蔡静颖,谢福鼎,张永..基于马氏距离特征加权的模糊聚类新算法[J].计算机工程与应用,2012,48(5):198-200,3.基金项目
国家自然科学基金(No.10771092) （No.10771092）
辽宁省科技厅博士启动基金(No.20081079) （No.20081079）
辽宁省教育厅高等学校科研项目资助(No.2008347). （No.2008347）
