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Advances in rough set theory and its appliations

HU Keyun, LU Yuchang, SHI Chunyi(Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China)  
Rough set theory, a new mathematical tool dealing with vagueness and uncertainty, was introduced by Pawlak in 1982. It has been widely used in the area of AI, data mining, pattern recognition, fault diagnositics, etc. This paper describes the basic algorithms for rough set theory, including equivalent relation, upper/lower approximation and reduction. Then several extensions of rough set theory are discussed such as VPRS, similarity based model, and applications of rough set theory in areas like data mining, rough logic, etc. Further research directions are then discussed.
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