Titlebook: Rough Set Methods and Applications; New Developments in Lech Polkowski,Shusaku Tsumoto,Tsau Y. Lin Book 2000 Springer-Verlag Berlin Heidel

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Knowledge Discovery by Application of Rough Set Models. These tools and techniques are the subject of the field of Knowledge Discovery in Databases. In this Chapter we discuss selected rough set based solutions to two main knowledge discovery problems, namely the description problem and the classification (prediction) problem.

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Various Approaches to Reasoning with Frequency Based Decision Reducts: A Surveying the frequency based information about . dependencies. Approximate criteria of preserving such a kind of information under attribute reduction are considered. These criteria are specified by using distances between frequency distributions and information measures related to different ways of interpreting rough membership based knowledge.

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Rough Mereology in Information Systems. A Case Study: Qualitative Spatial Reasoning with a more general functor of being a part in a degree. Rough Mereology has deep relations to Fuzzy Set Theory as it proposes to study the properties of partial containment which is also the fundamental subject of study for Fuzzy Set Theory.

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Rough Set Methods for the Synthesis and Analysis of Concurrent Processesll as self-implemented computer tools are used for this purpose. The methods presented in the paper as well as further investigations of interconnections between rough set theory and concurrency may stimulate the development of both theoretical and practical research related to the areas mentioned above.

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Rough Set Algorithms in Classification Problemsolving conflicts between decision rules classifying a new case to different categories (classes). Keywords: knowledge discovery, rough sets, classification algorithms, reducts, decision rules, real value attribute discretization

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