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. doi:10.1007/s11633-008-0090-3

Preview

PDF WRAP_Bean_IJAC_unedited.pdf - Requires a PDF viewer. Download (332Kb)

Request Changes to record.

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 Science, Engineering and Medicine > 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
Official Date:	January 2008
Dates:	Date Event January 2008 Published
Volume:	Vol.5
Number:	No.1
Page Range:	pp. 90-102
DOI:	10.1007/s11633-008-0090-3
Status:	Peer Reviewed
Access rights to Published version:	Open Access
Related URLs:	Publisher

Request changes or add full text files to a record

Repository staff actions (login required)

View Item

Downloads