吉林大学学报（理学版）2012，Vol.50Issue(6)：1179-1184,6.
一种高效鲁棒的无监督模糊c均值聚类算法
An Efficient and Robust Clustering Algorithm for Unsupervised Fuzzy c-Means
摘要
Abstract
On the condition of losing less information and retaining less data, the data were refined by the data reduction technique. The proposed approximation algorithm for fuzzy omeans clustering was used to estimate the cluster centers. Combined with validity indexed and estimated centers, FCM can execute unsupervised clustering. The proposed algorithm improved the computational efficiency and performance of the conventional unsupervised fuzzy c-means clustering algorithm. The contrast experimental results with conventional algorithms show that the proposed algorithm has a relatively high precision and efficiency. It can obtain the cluster number more accurately than the conventional algorithm.关键词
模糊c均值/聚类有效性/无监督聚类/数据约简Key words
fuzzy c-means/ cluster validity/ unsupervised clustering/ data reduction分类
信息技术与安全科学引用本文复制引用
曲福恒,胡雅婷,马驷良,郭世龙,李恒燕..一种高效鲁棒的无监督模糊c均值聚类算法[J].吉林大学学报（理学版）,2012,50(6):1179-1184,6.基金项目
国家自然科学基金(批准号:10926157)和国家"十一五"科技支撑计划项目(批准号:2009BAE69B01). （批准号:10926157）
