计算机工程与应用2019，Vol.55Issue(19)：58-65,8.DOI:10.3778/j.issn.1002-8331.1903-0292
特征逐减的可能性模糊聚类算法
Feature-Reduction Possibilistic Fuzzy Clustering Algorithm
摘要
Abstract
Fuzzy C-Means(FCM)algorithm and Possibilistic Fuzzy C-Means(PFCM)algorithm are sensitive to noise points because they do not consider the contribution of data features and individual data points. The Feature-Reduction Fuzzy C-Means(FRFCM)algorithm can remove the useless features of a dataset and compute the feature weights of remainders. So the FRFCM algorithm has better clustering performance. Based on the PFCM algorithm, a new Feature-Reduction Possibilistic Fuzzy C-Means(FRPFCM)algorithm is proposed. The FRPFCM algorithm not only solves the parameter dependency problem of the PFCM algorithm, but also can automatically weed out invalid data features and update the contribution degree to clustering of the rest data features. The experimental results on the synthetic and UCI datasets show that the proposed FRPFCM algorithm can get higher clustering precisions and need less iterations so that speed up its convergence rate.关键词
聚类分析/模糊聚类/可能性模糊聚类/特征逐减Key words
clustering analyses/fuzzy clustering/possibilistic fuzzy clustering/feature-reduction分类
信息技术与安全科学引用本文复制引用
余炳光,刘冬梅..特征逐减的可能性模糊聚类算法[J].计算机工程与应用,2019,55(19):58-65,8.基金项目
国家重点研发计划子课题(No.2017YFE0301105). （No.2017YFE0301105）
