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Knowledge Reduction of Covering Approximation Space

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Transactions on Computational Science V

Part of the book series: Lecture Notes in Computer Science ((TCOMPUTATSCIE,volume 5540))

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Abstract

Knowledge reduction is a key issue in data mining. In order to simplify the covering approximation space and mining rules from it, Zhu proposed a reduction of covering approximation space which does not rely on any prior given concept or decision. Unfortunately, it could only reduce absolutely redundant knowledge. To reduce relatively redundant knowledge with respect to a given concept or decision, the problem of relative reduction is studied in this paper. The condition in which an element of a covering is relatively reducible is discussed. By deleting all relatively reducible elements of a covering approximation space, one can get the relative reduction of the original covering approximation space. Moreover, one can find that the covering lower and upper approximations in the reduced space are the same as in the original covering space. That is to say, it does not decrease the classification ability of a covering approximation space to reduce the relatively reducible elements in it. In addition, combining absolute reduction and relative reduction, an algorithm for knowledge reduction of covering approximation space is developed. It can reduce not only absolutely redundant knowledge, but also relatively redundant knowledge. It is significant for the following-up steps of data mining.

This research is supported by the National Natural Science Foundation of P.R. China (No.60573068, No.60773113), Natural Science Foundation of Chongqing (No.2005BA2003, No.2008BA2017), Natural Science Foundation of Chongqing University of Posts and Telecommunications (A2006-56), and Science & Technology Research Program of the Municipal Education Committee of Chongqing (No.KJ060517).

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Authors and Affiliations

  1. School of Electronic Engineering, XiDian University, Xi’an, Shaanxi, 710071, P.R. China

    Jun Hu

  2. Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, P.R. China

    Jun Hu & GuoYin Wang

Authors
  1. Jun Hu
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  2. GuoYin Wang
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Editors and Affiliations

  1. University of Calgary, Department of Computer Science, 2500 University Drive N.W, T2N 1N4, Calgary, AB, Canada

    Marina L. Gavrilova

  2. OptimaNumerics Ltd., Cathedral House, 23-31 Waring Street, BT1 2DX, Belfast, UK

    C. J. Kenneth Tan

  3. Theoretical and Empirical Software Engineering Research Centre (TESERC), University of Calgary, 2500 University Drive, NW, T2N 1N4, Calgary, Alberta, Canada

    Yingxu Wang

  4. Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

    Keith C. C. Chan

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Hu, J., Wang, G. (2009). Knowledge Reduction of Covering Approximation Space. In: Gavrilova, M.L., Tan, C.J.K., Wang, Y., Chan, K.C.C. (eds) Transactions on Computational Science V. Lecture Notes in Computer Science, vol 5540. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02097-1_4

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  • DOI: https://doi.org/10.1007/978-3-642-02097-1_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02096-4

  • Online ISBN: 978-3-642-02097-1

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