Rough Set Theory Approach in Feature Selection and Clustering

Nilam Sachin Patil, E. Kannan

Abstract


Abstract—The Rough Set (RS) theory may be considered as a tool to reduce the input spatial property and to influence unclearness and uncertainty in datasets. Over the years, there has been arapid growth in interest in rough set theory and its applications in computer science and cognitive sciences,especially in analysis areas like machine learning,intelligent systems, colligation, pattern recognition,data pre-processing, data discovery, decision analysis,and knowledgeable systems. This paper discusses the fundamental ideas of rough pure mathematics and imply some rough set-based analysis directions and applications. The discussion additionally includes are view of rough set theory in numerous machine learningtechniques like clump, feature choice and rule induction.

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References


Hassanien, A. E., Suraj Z., Slezak, D. and Lingras, P.Rough Computing.

Theories, Technologies, andApplications, series: In memoriam Professor

Zdzislaw Pawlak, IGI Global Hershey, New York, 2008.

Lingras P. Unsupervised rough set classification using Gas. Journal of

Intelligent Information System, 16(2001), 215228.

Lingras, P., Hogo, M. and Snorek, M. Interval set clustering of web users

using modified Kohonen self-organizing maps based on the properties of

rough sets. Web Intelligence and Agent System: An InternationalJournal,

, 3(2004), 217230.

Lingras, P., Hogo, M., Snorek, M. And West, C. Temporal analysis

of clusters of supermarket customers: conventional versus interval set

approach. Information Sciences, 172(2005), 215240.

Kusiak, A.Rough set theory: A Data Mining tool for semiconductor

manufacturing. IEEE Transactions on Electronics Packaging Manufacturing,24,1(2001),

Peters, G. And Lampart, M. A partitive rough clustering algorithm.

Proceedings of the Fifth International Conference on Rough Sets and

Current Trends in Computing (RSCTC’06), Lecture Notes in Artificial

Intelligence, LNAI-4259, (Kobe, Japan,2006),Springer,657-666.

Peters, G.Rough clustering and regression analysis.in Proceedings of 2007

IEEE Conference on Rough Sets and Knowledge Technology (RSKT’07),

Lecture Notes in Artificial Intelligence, LNAI-4481, pp.292299 (Toronto,

Canada,2007), John Wiley & Sons Inc, 292299.

Peters, G., Lampart, M. and Weber, R. Evolutionary rough k-medoid

clustering. Transactions on Rough Sets VIII, Lecture Notes in Computer

Science, 5084(2008),289306

Pawlak, Z. Rough sets. International Journal of Computer and Information

Sciences, 11(1982), 341-356.

Hu, K. Y., Lu, Y. C. and Shi, C. Y. Feature ranking in rough sets.

Artificial Intelligence Communications, 16,1(2003), 4150.

Wang, Q. H. and Li, J. R. A rough set-based fault ranking prototype

system for fault diagnosis. Engineering Applications of Artificial Intelligence,

, 8(2004),909917.

Tsumoto, S. Extraction of Experts Decision Rules from Clinical

Databases using Rough Set Model. Journal of Intelligent Data Analysis,

, 3(1998), 215-227.

Law, R. and Au, N. Relationship modelling in tourism shopping:

A decision rules induction approach. Tourism Management, 21(2000),


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