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Pacific-Asia Conference on Knowledge Discovery and Data Mining

PAKDD 2002: Advances in Knowledge Discovery and Data Mining pp 405–416Cite as

Mining Interesting Rules in Meningitis Data by Cooperatively Using GDT-RS and RSBR

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2336))

Abstract

This paper describes an application of two rough sets based systems, namely GDT-RS and RSBR respectively, for mining if-then rules in a meningitis dataset. GDT-RS (Generalized Distribution Table and Rough Set) is a soft hybrid induction system, and RSBR (Rough Sets with Boolean Reasoning) is used for discretization of real valued attributes as a preprocessing step realized before the GDT-RS starts. We argue that discretization of continuous valued attributes is an important pre-processing step in the rule discovery process. We illustrate the quality of rules discovered by GDT-RS is strongly affected by the result of discretization.

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Author information

Authors and Affiliations

  1. Department of Information Engineering, Maebashi Institute of Technology, 460-1, Kamisadori-Cho, Maebashi-City, 371, Japan

    Ning Zhong & Juzhen Dong

Authors
  1. Ning Zhong
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  2. Juzhen Dong
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Editor information

Editors and Affiliations

  1. EE Department, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei, Taiwan, ROC

    Ming-Syan Chen

  2. IBM Thomas J. Watson Research Center, 30 Sawmill River Road, Hawthorne, NY, 10532, USA

    Philip S. Yu

  3. School of Computing, National University of Singapore, Lower Kent Ridge Road, Singapore, 119260

    Bing Liu

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© 2002 Springer-Verlag Berlin Heidelberg

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Zhong, N., Dong, J. (2002). Mining Interesting Rules in Meningitis Data by Cooperatively Using GDT-RS and RSBR. In: Chen, MS., Yu, P.S., Liu, B. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2002. Lecture Notes in Computer Science(), vol 2336. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-47887-6_40

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  • DOI: https://doi.org/10.1007/3-540-47887-6_40

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43704-8

  • Online ISBN: 978-3-540-47887-4

  • eBook Packages: Springer Book Archive

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