Fuzzy k-Means with Variable Weighting in High Dimensional Data Analysis

摘要：

This paper presents a comparison study of the fuzzy k-means algorithm and a new variant with variable weighting in clustering high dimensional data. The fuzzy k-means algorithm is effective in discovering the clusters with overlapping boundaries. However, this effectiveness can be handicapped in high dimensional data. The recent development of the k-means algorithm with automated variable weighting offers a new technique for dealing with high dimensional data that occurs in many new applications such as text mining and bioinformatics. In this paper, the variable weighting mechanism is incorporated in the fuzzy k-means algorithm to cluster high dimensional data with overlapping clusters. Experiments on real data sets have shown that the variable weighting fuzzy k-means produced better clustering results than the fuzzy k-means without variable weighting.

作者: Qiang Wang Yunming Ye Joshua Zhexue Huang

作者单位: Shenzhen Graduate School Harbin Institute of Technology Xili,Shenzhen 518055,China E-Business Technology Institute The University of Hong Kong Pokfulam Road,Hong Kong

会议类型: 国际会议

会议名称: The Ninth International Conference on Web-Age Information Management(第九届web时代信息管理国际会议)(WAIM 2008)

会议地点: 张家界

会议语种:英文

在线出版日期: 2008-07-20（万方平台首次上网日期，不代表论文的发表时间）