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Annual Conference of the Japanese Society for Artificial Intelligence

JSAI 2001: New Frontiers in Artificial Intelligence pp 516–524Cite as

Basket Analysis on Meningitis Data

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

Abstract

Basket Analysis is the most representative approach in recent study of data mining. However, it cannot be directly applied to the data including numeric attributes. In this paper, we propose an algorithm and performance measures for the selection and the discretization of numeric attributes in the data preprocessing stage for the wider application of Basket Analysis, and the performance is evaluated through the application to the meningitis data.

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References

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Author information

Authors and Affiliations

  1. Institute of Scientific and Industrial Research, Osaka University, 8-1, Mihogaoka, 567-0047, Ibarakishi, Osaka, Japan

    Takayuki Ikeda, Takashi Washio & Hiroshi Motoda

Authors
  1. Takayuki Ikeda
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  2. Takashi Washio
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  3. Hiroshi Motoda
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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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Cite this paper

Ikeda, T., Washio, T., Motoda, H. (2001). Basket Analysis on Meningitis Data. 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_72

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  • DOI: https://doi.org/10.1007/3-540-45548-5_72

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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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