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Rough Sets

Rough Sets

Zdzislaw Pawlak, Lech Polkowski, Andrzej Skowron
Copyright: © 2005 |Pages: 6
ISBN13: 9781591405603|ISBN10: 1591405602|EISBN13: 9781591407959
DOI: 10.4018/978-1-59140-560-3.ch095
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MLA

Pawlak, Zdzislaw, et al. "Rough Sets." Encyclopedia of Database Technologies and Applications, edited by Laura C. Rivero, et al., IGI Global, 2005, pp. 575-580. https://doi.org/10.4018/978-1-59140-560-3.ch095

APA

Pawlak, Z., Polkowski, L., & Skowron, A. (2005). Rough Sets. In L. Rivero, J. Doorn, & V. Ferraggine (Eds.), Encyclopedia of Database Technologies and Applications (pp. 575-580). IGI Global. https://doi.org/10.4018/978-1-59140-560-3.ch095

Chicago

Pawlak, Zdzislaw, Lech Polkowski, and Andrzej Skowron. "Rough Sets." In Encyclopedia of Database Technologies and Applications, edited by Laura C. Rivero, Jorge Horacio Doorn, and Viviana E. Ferraggine, 575-580. Hershey, PA: IGI Global, 2005. https://doi.org/10.4018/978-1-59140-560-3.ch095

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Abstract

Rough set theory is a new mathematical approach to imperfect knowledge. The problem of imperfect knowledge, tackled for a long time by philosophers, logicians, and mathematicians, has become also a crucial issue for computer scientists, particularly in the area of artificial intelligence. There are many approaches to the problem of how to understand and manipulate imperfect knowledge. The most successful one is, no doubt, fuzzy set theory proposed by Zadeh (1965). Rough set theory (Pawlak, 1982) presents still another attempt at this problem. This theory has attracted the attention of many researchers and practitioners all over the world, who contributed essentially to its development and applications. The rough set approach seems to be of fundamental importance to AI and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge discovery from databases, expert systems, inductive reasoning, and pattern recognition.

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