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On the Extension of Rough Sets under Incomplete Information

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New Directions in Rough Sets, Data Mining, and Granular-Soft Computing (RSFDGrC 1999)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1711))

Abstract

The rough set theory, based on the conventional indiscernibility relation, is not useful for analysing incomplete information. We introduce two generalizations of this theory. The first proposal is based on non symmetric similarity relations, while the second one uses valued tolerance relation. Both approaches provide more informative results than the previously known approach employing simple tolerance relation.

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Author information

Authors and Affiliations

  1. Institute of Computing Science, Poznań University of Technology, 3A Piotrowo, 60-965, Poznań, Poland

    Jerzy Stefanowski

  2. LAMSADE – CNRS Université Paris Dauphine, 75775, Paris Cédex 16, France

    Alexis Tsoukiàs

Authors
  1. Jerzy Stefanowski
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  2. Alexis Tsoukiàs
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Editor information

Editors and Affiliations

  1. The International WIC Institute, Beijing University of Technology, China

    Ning Zhong

  2. Institute of Mathematics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland

    Andrzej Skowron

  3. Professor Emeritus, University of Tokyo,  

    Setsuo Ohsuga

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© 1999 Springer-Verlag Berlin Heidelberg

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Stefanowski, J., Tsoukiàs, A. (1999). On the Extension of Rough Sets under Incomplete Information. In: Zhong, N., Skowron, A., Ohsuga, S. (eds) New Directions in Rough Sets, Data Mining, and Granular-Soft Computing. RSFDGrC 1999. Lecture Notes in Computer Science(), vol 1711. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-48061-7_11

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  • DOI: https://doi.org/10.1007/978-3-540-48061-7_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66645-5

  • Online ISBN: 978-3-540-48061-7

  • eBook Packages: Springer Book Archive

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