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Rough Sets In Data Analysis: Foundations and Applications

  • Chapter
Applications of Computational Intelligence in Biology

Part of the book series: Studies in Computational Intelligence ((SCI,volume 122))

Summary

Rough sets is a paradigm introduced in order to deal with uncertainty due to ambiguity of classification caused by incompleteness of knowledge. The idea proposed by Z. Pawlak in 1982 goes back to classical idea of representing uncertain and/or inexact notions due to the founder of modern logic, Gottlob Frege: uncertain notions should possess around them a region of uncertainty consisting of objects that can be qualified with certainty neither into the notion nor to its complement. The central tool in realizing this idea in rough sets is the relation of uncertainty based on the classical notion of indiscernibility due to Gottfried W. Leibniz: objects are indiscernible when no operator applied to each of them yields distinct values.

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Author information

Authors and Affiliations

  1. Polish-Japanese Institute of Information Technology, Koszykowa 86, 02008, Warszawa, Poland

    Lech Polkowski

  2. Department of Mathematics and Computer Science, University of Warmia and Mazury, Żołnierska 14, Olsztyn, Poland

    Lech Polkowski & Piotr Artiemjew

Authors
  1. Lech Polkowski
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  2. Piotr Artiemjew
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Editor information

Editors and Affiliations

  1. Department of Biology, Emory University, 1510 Clifton Road NE, Atlanta, Georgia, 30322, USA

    Tomasz G. Smolinski

  2. Department of Computer Science, University of Arkansas at Little Rock, 2801 S. University Ave., Little Rock, Arkansas, 72204, USA

    Mariofanna G. Milanova

  3. Department of Quantitative Methods and Information Systems College of Business and Administration, Kuwait University, P.O. Box 5486, Safat, 13055, Kuwait

    Aboul-Ella Hassanien

  4. Department of Information Technology Faculty of Computers and Information, Cairo University, 5 Ahamed Zewal Street, Orman, Giza, Egypt

    Aboul-Ella Hassanien

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Polkowski, L., Artiemjew, P. (2008). Rough Sets In Data Analysis: Foundations and Applications. In: Smolinski, T.G., Milanova, M.G., Hassanien, AE. (eds) Applications of Computational Intelligence in Biology. Studies in Computational Intelligence, vol 122. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78534-7_2

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  • DOI: https://doi.org/10.1007/978-3-540-78534-7_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-78533-0

  • Online ISBN: 978-3-540-78534-7

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