International Journal of Automation and Computing

, Volume 5, Issue 1, pp 90–102

Autonomous clustering using rough set theory

Authors

  • Charlotte Bean
    • Warwick Medical School Gibbet Hill CampusUniversity of Warwick
    • Department of Computer ScienceUniversity of Hull
Article

DOI: 10.1007/s11633-008-0090-3

Cite this article as:
Bean, C. & Kambhampati, C. Int. J. Autom. Comput. (2008) 5: 90. doi:10.1007/s11633-008-0090-3

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.

Keywords

Rough set theory (RST) data clustering knowledge-oriented clustering autonomous

Copyright information

© Institute of Automation, Chinese Academy of Sciences 2008