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Author(s): Lingras PJ; Yao YY
Title: Data mining using extensions of the rough set model
Source: JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE 49 (5): 415-422
Date: 1998 APR 15
Document Type: Journal : Article
Language: English
Comment:
Address: Algoma Univ Coll, Dept Comp Sci, Sault St Marie, ON P6A 2G4, Canada.
Lakehead Univ, Dept Comp Sci, Thunder Bay, ON P7B 5E1, Canada. Reprint: Lingras, PJ, Algoma Univ Coll, Dept Comp Sci, Sault St Marie, ON P6A
2G4, Canada. Abstract: This article examines basic issues of data mining using the theory of rough sets, which is a recent proposal for generalizing classical set theory. The Pawlak rough set model is based on the concept of an equivalence relation. Recent research has shown that a generalized rough set model need not be based on equivalence relation axioms. The Pawlak rough set model has been used for deriving deterministic as well as probabilistic rules from a complete database. This article demonstrates that a generalized rough set model can be used for generating rules from incomplete databases. These rules are based on plausibility functions proposed by Shafer. The article also discusses the importance of rule extraction from incomplete databases in data mining.
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