Correlating Fuzzy and Rough Clustering

  • Authors:

  • Manish Joshi;Pawan Lingras;C. Raghavendra Rao

  • Affiliations:

  • (Correspd.) Department of Computer Science, North Maharashtra University, Jalgaon, Maharashtra, India. joshmanish@gmail.com;Department of Mathematics and Computing Science, Saint Mary's University, Halifax, Nova Scotia, B3H 3C3, Canada. pawan@cs.smu.ca;Department of Computer and Information Sciences, University of Hyderabad, Hyderabad, Andhra Pradesh, India. crrcs@uohyd.ernet.in

  • Venue:
  • Fundamenta Informaticae - Rough Sets and Knowledge Technology (RSKT 2010)
  • Year:
  • 2012

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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. Many applications demand use of combined approach to exploit inherent strengths of each technique. Our objective is to examine correlation between these two techniques. This paper provides an experimental description of how rough clustering results can be correlated with fuzzy clustering results. We illustrate procedural steps to map 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.