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Annual Conference of the Japanese Society for Artificial Intelligence

JSAI 2001: New Frontiers in Artificial Intelligence pp 380–384Cite as

Association Rules in Semantically Rich Relations: Granular Computing Approach

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2253))

Abstract

In “real world” databases, attribute domains are more than Cantor sets; the additional semantics defined, in this paper, is assumed to be carried by a binary relation. Association rules in such databases are investigated. In this paper, we show that the cost of checking the additional semantics is rather small. Some experiments are reported.

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References

  1. T. Y. Lin, “Data Mining and Machine Oriented Modeling: A Granular Computing Approach,” Journal of Applied Intelligence, Kluwer, Vol. 13, No 2, September/October, 2000, pp.113–124.

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  2. T. Y. Lin, “Data Mining: Granular Computing Approach.” In: Methodologies for Knowledge Discovery and Data Mining, Lecture Notes in Artificial Intelligence 1574, Third Pacific-Asia Conference, Beijing, April 26–28, 1999, 24–33.

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  3. T. Y. Lin, “Granular Computing on Binary Relations I: Data Mining and Neighborhood Systems.” In: Rough Sets In Knowledge Discovery, A. Skoworn and L. Polkowski (eds), Springer-Verlag, 1998, 107–121.

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  4. T. Y. Lin, “Neighborhood Systems and Relational Database”. Abstract, Proceedings of CSC’ 88, February, 1988, pp. 725.

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  5. Eric Louie and T.Y. Lin, “Finding Association Rules using Fast Bit Computation: Machine-Oriented Modeling.” In: Proceeding of 12th International Symposium ISMIS2000, Charlotte, North Carolina, Oct 11–14, 2000. Lecture Notes in AI 1932. 486–494.

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  6. T. Y. Lin and E. Louie, “A Data Mining Approach using Machine Oriented Modeling: Finding Association Rules using Canonical Names.”. In: Proceeding of 14th Annual International Symposium Aerospace/Defense Sensing, Simulation, and Controls, SPIE Vol 4057, Orlando, April 24–28, 2000, pp.148–154

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  7. Balaji Padmanabhan and Alexander Tuzhilin “Finding Unexpected Patterns in Data.” In: Data Mining and Granular Computing T. Y. Lin, Y.Y. Yao and L. Zadeh (eds), Physica-Verlag, to appear.

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Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, San Jose State University, 95192-0103, San Jose, California

    T. Y. Lin

  2. IBM Almaden Research Center, 650 Harry Road, 95120, San Jose, CA

    Eric Louie

Authors
  1. T. Y. Lin
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  2. Eric Louie
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Editor information

Editors and Affiliations

  1. Graduate School of Business Science, The University of Tsukuba, Tokyo

    Takao Terano  & Yukio Ohsawa  & 

  2. School of Engineering, The University of Tokyo, Tokyo

    Toyoaki Nishida

  3. Dept.of Computer Science, National Defense Academy, Japan

    Akira Namatame

  4. School of Medicines, Shimane Medical University, Japan

    Syusaku Tsumoto

  5. The Institute of Scientific and Industrial Research, Osaka University, Osaka

    Takashi Washio

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© 2001 Springer-Verlag Berlin Heidelberg

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Lin, T.Y., Louie, E. (2001). Association Rules in Semantically Rich Relations: Granular Computing Approach. In: Terano, T., Ohsawa, Y., Nishida, T., Namatame, A., Tsumoto, S., Washio, T. (eds) New Frontiers in Artificial Intelligence. JSAI 2001. Lecture Notes in Computer Science(), vol 2253. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45548-5_50

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  • DOI: https://doi.org/10.1007/3-540-45548-5_50

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43070-4

  • Online ISBN: 978-3-540-45548-6

  • eBook Packages: Springer Book Archive

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