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GRS: A Generalized Rough Sets Model

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Data Mining, Rough Sets and Granular Computing

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 95))

Abstract

Rough sets extends classical set theory by incorporating the set model into the notion of classification in the form of an indiscernibility relation. Rough sets serves as a tool for data analysis and knowledge discovery from databases. A generalized rough sets model, based on the concept of the VPRS-model, is proposed in this paper. Our approach modifies the traditional rough sets model and is aimed at handling uncertain objects by considering the importance of each object while reducing the influence of noise in modelling the classification process.

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References

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Author information

Authors and Affiliations

  1. Knowledge Stream Partner, 148 State St., Boston, MA, 02109, USA

    Xiaohua Hu

  2. Dept. of Computer Science, Unvi. of Waterloo, Waterloo, Ont., Canada

    Nick Cercone & Jianchao Han

  3. Dept. of Computer Science, Univ. of Regina, Regina, Sask., Canada

    Wojciech. Ziarko

Authors
  1. Xiaohua Hu
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  2. Nick Cercone
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  3. Jianchao Han
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  4. Wojciech. Ziarko
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Editor information

Editors and Affiliations

  1. Department of Mathematics and Computer Science, San Jose State University The Metropolitan University of Silicon Valley, One Washington Square, 95192-0103, San Jose, CA, USA

    Tsau Young Lin

  2. Department of Computer Science, University of Regina, S4S 0A2, Regina, Saskatchewan, Canada

    Yiyu Y. Yao

  3. Computer Science Division and Electronics Research Laboratory Department of Electrical and Electronics, University of California Berkeley Initiative in Soft Computing (BISC), 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

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© 2002 Springer-Verlag Berlin Heidelberg

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Hu, X., Cercone, N., Han, J., Ziarko, W. (2002). GRS: A Generalized Rough Sets Model. In: Lin, T.Y., Yao, Y.Y., Zadeh, L.A. (eds) Data Mining, Rough Sets and Granular Computing. Studies in Fuzziness and Soft Computing, vol 95. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1791-1_21

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  • DOI: https://doi.org/10.1007/978-3-7908-1791-1_21

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-2508-4

  • Online ISBN: 978-3-7908-1791-1

  • eBook Packages: Springer Book Archive

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