Rough Set Theory Approach in Feature Selection and Clustering

Nilam Sachin Patil, E. Kannan


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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