现代电子技术2017，Vol.40Issue(21)：112-116,5.DOI:10.16652/j.issn.1004-373x.2017.21.031
基于多属性模糊C均值聚类的属性约简算法
Attribute reduction algorithm based on multiattribute fuzzy C-means clustering
摘要
Abstract
The fuzzy C-means clustering algorithm used to process the high-dimensional datasets has the problems of high computational complexity,poor algorithm generalization ability and low calculation accuracy. Considering the difference of fea-ture attribute for clustering contribution,a new reduction algorithm based on attribute importance is proposed on the basis of the thought of multiattribute fuzzy C-means clustering. In order to verify its validity,the comparative analysis was performed in UCI datasets for the proposed algorithm,factor analysis method and reduction method based on rough set theory. The experimental results show this method has wider application range,and better performance on the datasets whose average standard deviation is large or the inter-class centre distance is far.关键词
数据挖掘/模糊C均值聚类/属性约简/聚类效果Key words
data mining/fuzzy C-means clustering/attribute reduction/clustering effect分类
信息技术与安全科学引用本文复制引用
李诗瑾,李倩,徐桂琼..基于多属性模糊C均值聚类的属性约简算法[J].现代电子技术,2017,40(21):112-116,5.基金项目
国家自然科学基金(11201290) （11201290）
