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Attribute Reduction in Concept Lattice Based on Discernibility Matrix

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Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing (RSFDGrC 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3642))

Abstract

As an effective tool for knowledge discovery, concept lattice has been successfully applied to various fields. One of the key problems of knowledge discovery is knowledge reduction. This paper studies attribute reduction in concept lattice. Using the idea similar to Skowron and Rauszer’s discernibility matrix, the discernibility matrix and function of a concept lattice are defined. Based on discernibility matrix, an approach to attribute reduction in concept lattice is presented, and the characteristics of core attribute are analyzed.

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Author information

Authors and Affiliations

  1. Faculty of Science, Institute for Information and System Sciences, Xi’an Jiaotong University, Xi’an, 710049, P.R. China

    Wen-Xiu Zhang & Ling Wei

  2. Department of Mathematics, Northwest University, Xi’an, 710069, P.R. China

    Ling Wei

  3. Institute of Computer Architecture & Network, Xi’an Jiaotong University, Xi’an, 710049, P.R. China

    Jian-Jun Qi

Authors
  1. Wen-Xiu Zhang
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  2. Ling Wei
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  3. Jian-Jun Qi
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Regina, Regina, SK, S4S 0A2 Canada, Polish-Japanese Institute of Information Technology, Koszykowa 86, 02-008 Warsaw, P.O. Box, Poland

    Dominik Ślęzak

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

    JingTao Yao  & Wojciech Ziarko  & 

  3. Department of Electrical and Computer Engineering, University of Manitoba, R3T 5V6, Winnipeg, Manitoba, Canada

    James F. Peters

  4. College of Computer and Information Engineering, Hehan University, Henan, China

    Xiaohua Hu

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© 2005 Springer-Verlag Berlin Heidelberg

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Zhang, WX., Wei, L., Qi, JJ. (2005). Attribute Reduction in Concept Lattice Based on Discernibility Matrix. In: Ślęzak, D., Yao, J., Peters, J.F., Ziarko, W., Hu, X. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2005. Lecture Notes in Computer Science(), vol 3642. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11548706_17

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  • DOI: https://doi.org/10.1007/11548706_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28660-8

  • Online ISBN: 978-3-540-31824-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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