Autonomous clustering using rough set theory

Bean, Charlotte; Kambhampati, Chandra

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. © 2008 Institute of Automation, Chinese Academy of Sciences.

Citation

Bean, C., & Kambhampati, C. (2008). Autonomous clustering using rough set theory. International Journal of Automation and Computing, 5(1), 90-102. https://doi.org/10.1007/s11633-008-0090-3

• Journal Article Type: Article
• Publication Date: 2008-01
• Deposit Date: Nov 13, 2014
• Journal: International Journal of Automation and Computing
• Print ISSN: 1476-8186
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 5
• Issue: 1
• Pages: 90-102
• DOI: https://doi.org/10.1007/s11633-008-0090-3
• Keywords: Control and Systems Engineering; Modelling and Simulation; Applied Mathematics; Computer Science Applications
• Public URL: https://hull-repository.worktribe.com/output/464656
• Contract Date: Nov 13, 2014