Rough Computing: Theories, Technologies and Applications by by Aboul Ella Hassanien, Aboul Ella Hassanien, Zbigniew Suraj,

By Aboul Ella Hassanien, Aboul Ella Hassanien, Zbigniew Suraj, Dominik Slezak, Pawan Lingras

Tough set concept is a brand new smooth computing device which bargains with vagueness and uncertainty. It has attracted the eye of researchers and practitioners around the world, and has been effectively utilized to many fields corresponding to wisdom discovery, choice aid, trend popularity, and computer studying.

Rough Computing: Theories, applied sciences and purposes deals the main accomplished assurance of key tough computing learn, surveying an entire diversity of subject matters from granular computing to pansystems idea. With its special insurance of the defining problems with the sector, this commanding examine assortment offers libraries with a unmarried, authoritative connection with this hugely complicated technological subject.

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1903). Grundgesetzen der Arithmetik, 2. Jena, Germany: Verlag von Herman Pohle. , & Gediga, G. (1997). Algebraic aspects of attribute dependencies in information systems. 29, 119-133. , & Walker, E. (1992). On the structure of rough sets. of. Sciences, Mathematics, 40, 235-245. Gratzer, G. (1971). Lattice theory: First concepts and. distirbutive. lattices. San Francisco: W. H. Freeman and Company. Gratzer, G. (1978). General lattice theory. Basel, Switzerland: Birkhauser. , & Słowiński, R. (2002).

2002). Generalized decision algorithms, rough inference rules and flow graphs. J. F. Peters, A. Skowron, N. ), Rough. sets. and. current trends in computing, 3rd. International. Conference (RSCTC’2002). Lectures Notes in Artificial Intelligence, 2475 (pp. 93-104). Berlin, Heildelberg, Germany: Springer-Verlag. Grzymała-Busse, J. W. (1988). Knowledge acquisition under uncertainty - A rough set approach. 1(1), 3-16. , & Hu, M. (2001). A comparison of several approaches to missing attribute values in data mining.

J. F. Peters, A. Skowron, N. ), Rough. sets. and. current trends in computing, 3rd. International. Conference (RSCTC’2002). Lectures Notes in Artificial Intelligence, 2475 (pp. 93-104). Berlin, Heildelberg, Germany: Springer-Verlag. Grzymała-Busse, J. W. (1988). Knowledge acquisition under uncertainty - A rough set approach. 1(1), 3-16. , & Hu, M. (2001). A comparison of several approaches to missing attribute values in data mining. In W. Ziarko, & Y. International. Trends. in Computing (RSCTC’2000).

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