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

Authors:
Qiang Wang;Yunming Ye;Joshua Zhexue Huang
Affiliations:
-;-;-
Venue:
WAIM '08 Proceedings of the 2008 The Ninth International Conference on Web-Age Information Management
Year:
2008

Citing 0
Cited 3

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Abstract

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.