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Rough Set Attribute Reduction in Decision Systems

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Rough Sets and Knowledge Technology (RSKT 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4062))

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Abstract

An important issue of knowledge discovery and data mining is the reduction of pattern dimensionality. In this paper, we investigate the attribute reduction in decision systems based on a congruence on the power set of attributes and present a method of determining congruence classifications. We can obtain the reducts of attributes in decision systems by using the classification. Moreover, we prove that the reducts obtained by the congruence classification coincide with the distribution reducts in decision systems.

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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, Shaan’xi, 710049, P.R. China

    Hongru Li, Wenxiu Zhang, Ping Xu & Hong Wang

Authors
  1. Hongru Li
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  2. Wenxiu Zhang
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  3. Ping Xu
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  4. Hong Wang
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Editor information

Editors and Affiliations

  1. College of Computer Science and Technology, Chongqing, University of Posts and Telecommunication, 400065, Chongqing, P.R. China

    Guo-Ying Wang

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

    James F. Peters

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

    Andrzej Skowron

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

    Yiyu Yao

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© 2006 Springer-Verlag Berlin Heidelberg

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Li, H., Zhang, W., Xu, P., Wang, H. (2006). Rough Set Attribute Reduction in Decision Systems. In: Wang, GY., Peters, J.F., Skowron, A., Yao, Y. (eds) Rough Sets and Knowledge Technology. RSKT 2006. Lecture Notes in Computer Science(), vol 4062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11795131_20

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  • DOI: https://doi.org/10.1007/11795131_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-36297-5

  • Online ISBN: 978-3-540-36299-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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