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Data mining using extensions of the rough set model

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  • P. J. Lingras
  • Y. Y. Yao

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.

Suggested Citation

  • P. J. Lingras & Y. Y. Yao, 1998. "Data mining using extensions of the rough set model," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 49(5), pages 415-422.
    • Handle: RePEc:bla:jamest:v:49:y:1998:i:5:p:415-422
    DOI: 10.1002/(SICI)1097-4571(19980415)49:53.0.CO;2-Z
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    Cited by:

    1. Leung, Yee & Wu, Wei-Zhi & Zhang, Wen-Xiu, 2006. "Knowledge acquisition in incomplete information systems: A rough set approach," European Journal of Operational Research, Elsevier, vol. 168(1), pages 164-180, January.
    2. Leung, Yee & Fischer, Manfred M. & Wu, Wei-Zhi & Mi, Ju-Sheng, 2008. "A rough set approach for the discovery of classification rules in interval-valued information systems," MPRA Paper 77767, University Library of Munich, Germany.
    3. Chun-Che Huang & Wen-Yau Liang & Roger R. Gung & Pei-An Wang, 2023. "Rough-Set-Based Rule Induction with the Elimination of Outdated Big Data: Case of Renewable Energy Equipment Promotion," Sustainability, MDPI, vol. 15(20), pages 1-19, October.

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