A fuzzy subspace algorithm for clustering high dimensional data

Authors:
Guojun Gan;Jianhong Wu;Zijiang Yang
Affiliations:
Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada;Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada;School of Information Technology, Atkinson Faculty of Liberal and Professional Studies, York University, Toronto, Ontario, Canada
Venue:
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Year:
2006

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Abstract

In fuzzy clustering algorithms each object has a fuzzy membership associated with each cluster indicating the degree of association of the object to the cluster. Here we present a fuzzy subspace clustering algorithm, FSC, in which each dimension has a weight associated with each cluster indicating the degree of importance of the dimension to the cluster. Using fuzzy techniques for subspace clustering, our algorithm avoids the difficulty of choosing appropriate cluster dimensions for each cluster during the iterations. Our analysis and simulations strongly show that FSC is very efficient and the clustering results produced by FSC are very high in accuracy.