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Data Mining Based on Rough Sets

Jerzy W. Grzymala-Busse, Wojciech Ziarko

Source Title: Data Mining: Opportunities and Challenges

ISBN13: 9781591400516|ISBN10: 1591400511|ISBN13 Softcover: 9781931777834|EISBN13: 9781591400950

DOI: 10.4018/978-1-59140-051-6.ch006

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MLA

Grzymala-Busse, Jerzy W., and Wojciech Ziarko. "Data Mining Based on Rough Sets." Data Mining: Opportunities and Challenges, edited by John Wang, IGI Global, 2003, pp. 142-173. https://doi.org/10.4018/978-1-59140-051-6.ch006

APA

Grzymala-Busse, J. W. & Ziarko, W. (2003). Data Mining Based on Rough Sets. In J. Wang (Ed.), Data Mining: Opportunities and Challenges (pp. 142-173). IGI Global. https://doi.org/10.4018/978-1-59140-051-6.ch006

Chicago

Grzymala-Busse, Jerzy W., and Wojciech Ziarko. "Data Mining Based on Rough Sets." In Data Mining: Opportunities and Challenges, edited by John Wang, 142-173. Hershey, PA: IGI Global, 2003. https://doi.org/10.4018/978-1-59140-051-6.ch006

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Abstract

The chapter is focused on the data mining aspect of the applications of rough set theory. Consequently, the theoretical part is minimized to emphasize the practical application side of the rough set approach in the context of data analysis and model-building applications. Initially, the original rough set approach is presented and illustrated with detailed examples showing how data can be analyzed with this approach. The next section illustrates the Variable Precision Rough Set Model (VPRSM) to expose similarities and differences between these two approaches. Then, the data mining system LERS, based on a different generalization of the original rough set theory than VPRSM, is presented. Brief descriptions of algorithms are also cited. Finally, some applications of the LERS data mining system are listed.

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Data Mining Based on Rough Sets

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