Titlebook: Rough Sets in Knowledge Discovery 2; Applications, Case S Lech Polkowski,Andrzej Skowron Book 1998 Physica-Verlag Heidelberg 1998 artificia

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Data Mining: A Probabilistic Rough Set Approachs can be flexibly selected, background knowledge can be used to constrain rule generation, and . rules with strengths can be effectively acquired from large, complex databases in an incremental, bottom-up mode. In this paper, we focus on basic concepts and an implementation of our methodology.

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Rough Sets in Industrial Applicationsg human operators of plants and processes and discovering rules governing their actions. The behavior of operators can often be specified by ., defined as sets of . coupled with rule selection mechanisms. Rough set theory [10, 11] can be used to generate such tables from ., containing the decisions of human operators [8].

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