Analysis of rough and fuzzy clustering

Authors:
Manish Joshi;Pawan Lingras;C. Raghavendra Rao
Affiliations:
Department of Computer Science, North Maharashtra University, Jalgaon, Maharashtra, India;Department of Mathematics and Computing Science, Saint Mary's University, Halifax, Nova Scotia, Canada;Department of Computer and Information Sciences, University of Hyderabad, Hyderabad, Andhra Pradesh, India
Venue:
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
Year:
2010

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Abstract

With the gaining popularity of rough clustering, soft computing research community is studying relationships between rough and fuzzy clustering as well as their relative advantages. Both rough and fuzzy clustering are less restrictive than conventional clustering. Fuzzy clustering memberships are more descriptive than rough clustering. In some cases, descriptive fuzzy clustering may be advantageous, while in other cases it may lead to information overload. This paper provides an experimental comparison of both the clustering techniques and describes a procedure for conversion from fuzzy membership clustering to rough clustering. However, such a conversion is not always necessary, especially if one only needs lower and upper approximations. Experiments also show that descriptive fuzzy clustering may not always (particularly for high dimensional objects) produce results that are as accurate as direct application of rough clustering. We present analysis of the results from both the techniques.