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

Darshit Parmar, Teresa Wu, Jennifer Blackhurst

Research output: Contribution to journalArticlepeer-review

138 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)879-893
Number of pages15
JournalData and Knowledge Engineering
Volume63
Issue number3
DOIs
StatePublished - Dec 2007

Keywords

  • Categorical data
  • Cluster analysis
  • Data mining
  • Rough Set Theory

ASJC Scopus subject areas

  • Information Systems and Management

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