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Data Mining: Granular Computing Approach

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Methodologies for Knowledge Discovery and Data Mining (PAKDD 1999)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1574))

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Abstract

In the relational database theory, it is assumed that the universe U to be represented is a set. The classical data mining took such assumption. In real life applications, the entities are often related. A “new ” data mining theory is explored with such additional semantics.

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Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, San Jose State University, San Jose, California, 95192

    T. Y. Lin

  2. Berkeley Initiative in Soft Computing, Department of Electrical Engineering and Computer Science, University of California, Berkeley, California, 94720

    T. Y. Lin

Authors
  1. T. Y. Lin
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Systems Engineering, Yamaguchi University, Tokiwa-Dai, 2557, Ube, 755, Japan

    Ning Zhong

  2. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Lizhu Zhou

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© 1999 Springer-Verlag Berlin Heidelberg

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Lin, T.Y. (1999). Data Mining: Granular Computing Approach. In: Zhong, N., Zhou, L. (eds) Methodologies for Knowledge Discovery and Data Mining. PAKDD 1999. Lecture Notes in Computer Science(), vol 1574. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48912-6_5

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  • DOI: https://doi.org/10.1007/3-540-48912-6_5

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65866-5

  • Online ISBN: 978-3-540-48912-2

  • eBook Packages: Springer Book Archive

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