Autonomous clustering using rough set theory

Charlotte Price, Chandra Kambhampati

Research output: Contribution to journalArticle

32 Citations (Scopus)

Abstract

This paper proposes a clustering technique that minimizes the need for subjective human intervention and is based on elements of rough set theory (RST). The proposed algorithm is unified in its approach to clustering and makes use of both local and global data properties to obtain clustering solutions. It handles single-type and mixed attribute data sets with ease. The results from three data sets of single and mixed attribute types are used to illustrate the technique and establish its efficiency.

Original languageEnglish
Pages (from-to)90-102
Number of pages13
JournalInternational Journal of Automation and Computing
Volume5
Issue number1
DOIs
Publication statusPublished - 1 Jan 2008
Externally publishedYes

Keywords

  • Autonomous
  • Data clustering
  • Knowledge-oriented clustering
  • Rough set theory (RST)

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modelling and Simulation
  • Computer Science Applications
  • Applied Mathematics

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