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Autonomous clustering using rough set theory

Bean, Charlotte; Kambhampati, Chandra

Authors

Charlotte Bean



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
Electronic ISSN 1751-8520
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