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Pacific-Asia Conference on Knowledge Discovery and Data Mining

PAKDD 1999: Methodologies for Knowledge Discovery and Data Mining pp 44–53Cite as

Mining Association Rules on Related Numeric Attributes

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

Abstract

In practical applications, some property is represented by a pair of related attributes. For example, blood pressure, temperature changes etc. The existing data mining approaches for association rules can not tackle those cases, because they treat every attribute independently. In this paper, as a special kind of correlation, we express the pair of attributes as a range-type attribute. We define a set of fuzzified relations between ranges and revise the definition of association rules. We also propose effective algorithms to evaluate the measures for ranking association rules on related numeric attributes.

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References

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Author information

Authors and Affiliations

  1. Department of Intelligence and Computer Science, Nagoya Institute of Technology, Nagoya, Japan

    Xiaoyong Du, Zhibin Liu & Naohiro Ishii

Authors
  1. Xiaoyong Du
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  2. Zhibin Liu
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  3. Naohiro Ishii
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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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Cite this paper

Du, X., Liu, Z., Ishii, N. (1999). Mining Association Rules on Related Numeric Attributes. 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_7

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  • DOI: https://doi.org/10.1007/3-540-48912-6_7

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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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