Bean, Charlotte and Kambhampati, Chandra. (2008) Autonomous clustering using rough set theory. International Journal of Automation and Computing , Vol.5 (No.1). pp. 90-102. ISSN 1476-8186

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Abstract

This paper proposes a clustering technique that minimises the need for subjective human intervention and is based on elements of rough set theory. 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 and results from three data sets of single and mixed attribute types are used to illustrate the technique and establish its efficiency.

Item Type:	Journal Article
Subjects:	Q Science > QA Mathematics
Divisions:	Faculty of Medicine > Warwick Medical School
Library of Congress Subject Headings (LCSH):	Rough sets, Cluster analysis
Journal or Publication Title:	International Journal of Automation and Computing
Publisher:	Springer Verlag
ISSN:	1476-8186
Date:	January 2008
Volume:	Vol.5
Number:	No.1
Page Range:	pp. 90-102
Identification Number:	10.1007/s11633-008-0090-3
Status:	Peer Reviewed
Access rights to Published version:	Open Access
Related URLs:	Publisher
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URI:	http://wrap.warwick.ac.uk/id/eprint/61

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