Approximations in Rough Sets vs Granular Computing for Coverings

Approximations in Rough Sets vs Granular Computing for Coverings

Guilong Liu, William Zhu
ISBN13: 9781466617438|ISBN10: 1466617438|EISBN13: 9781466617445
DOI: 10.4018/978-1-4666-1743-8.ch011
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MLA

Liu, Guilong, and William Zhu. "Approximations in Rough Sets vs Granular Computing for Coverings." Developments in Natural Intelligence Research and Knowledge Engineering: Advancing Applications, edited by Yingxu Wang, IGI Global, 2012, pp. 152-163. https://doi.org/10.4018/978-1-4666-1743-8.ch011

APA

Liu, G. & Zhu, W. (2012). Approximations in Rough Sets vs Granular Computing for Coverings. In Y. Wang (Ed.), Developments in Natural Intelligence Research and Knowledge Engineering: Advancing Applications (pp. 152-163). IGI Global. https://doi.org/10.4018/978-1-4666-1743-8.ch011

Chicago

Liu, Guilong, and William Zhu. "Approximations in Rough Sets vs Granular Computing for Coverings." In Developments in Natural Intelligence Research and Knowledge Engineering: Advancing Applications, edited by Yingxu Wang, 152-163. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-4666-1743-8.ch011

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Abstract

Rough set theory is an important technique in knowledge discovery in databases. Classical rough set theory proposed by Pawlak is based on equivalence relations, but many interesting and meaningful extensions have been made based on binary relations and coverings, respectively. This paper makes a comparison between covering rough sets and rough sets based on binary relations. This paper also focuses on the authors’ study of the condition under which the covering rough set can be generated by a binary relation and the binary relation based rough set can be generated by a covering.

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