Data mining using extensions of the rough set model

P. J. Lingras and Y. Y. Yao

Journal of the American Society for Information Science, 1998, vol. 49, issue 5, 415-422

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. © 1998 John Wiley & Sons, Inc.

Date: 1998
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https://doi.org/10.1002/(SICI)1097-4571(19980415)49:53.0.CO;2-Z

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Persistent link: https://EconPapers.repec.org/RePEc:bla:jamest:v:49:y:1998:i:5:p:415-422

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