MMR: An algorithm for clustering categorical data using Rough Set Theory

Authors:
Darshit Parmar;Teresa Wu;Jennifer Blackhurst
Affiliations:
Department of Industrial Engineering, PO Box 875906, Arizona State University, Tempe, AZ 85287-5906, USA;Department of Industrial Engineering, PO Box 875906, Arizona State University, Tempe, AZ 85287-5906, USA;Department of Industrial Engineering, PO Box 875906, Arizona State University, Tempe, AZ 85287-5906, USA
Venue:
Data & Knowledge Engineering
Year:
2007

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Abstract

A variety of cluster analysis techniques exist to group objects having similar characteristics. However, the implementation of many of these techniques is challenging due to the fact that much of the data contained in today's databases is categorical in nature. While there have been recent advances in algorithms for clustering categorical data, some are unable to handle uncertainty in the clustering process while others have stability issues. This research proposes a new algorithm for clustering categorical data, termed Min-Min-Roughness (MMR), based on Rough Set Theory (RST), which has the ability to handle the uncertainty in the clustering process.