, Volume 5, Issue 1, pp 90-102

Autonomous clustering using rough set theory

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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.

Charlotte Bean received her Master of mathematics (MMath) degree from the University of Hull, UK, in 1999 and the Ph.D. degree in computer science from the same university in 2004. She is currently a research fellow in the Medical School at the University of Warwick, UK.
Her research interests include data analysis, the theoretical development of techniques and algorithms used in statistical modeling and data mining, especially using tools such as rough set theory, FST, and artificial neural networks.
Chandra Kambhampati received his Ph.D. degree from City University, London, for his dissertation on algorithms for optimizing control in 1988. He hold positions in the Department of Cybernetics, at the University of Reading, and the Department of Chemical and Process Engineering, at the University of Newcastle Upon Tyne. He is currently a reader in the Department of Computer Science at the University of Hull. He also heads the Neural, Emergent and Agent Technologies (NEAT) Group, and has a number of doctoral and graduate students, and research associates. He has authored and co-authored over 100 papers on optimization, adaptive optimization, optimal control, neural networks, and fuzzy logic for control and robotics. He is a member of IEEE and IEE.
His research interests include fault tolerant control, networked control systems, signal processing, neural networks, and multiagent systems.