A nonlinear model to rank association rules based on semantic similarity and genetic network programing

Authors

  • Guangfei Yang,

    Non-member
    1. Graduate School of Information, Production and Systems, Waseda University Hibikino 2-7, Wakamatsu-ku, Kitakyushu, Fukuoka, 808-0135, Japan
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  • Kaoru Shimada,

    Member
    1. Graduate School of Information, Production and Systems, Waseda University Hibikino 2-7, Wakamatsu-ku, Kitakyushu, Fukuoka, 808-0135, Japan
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  • Shingo Mabu,

    Member
    1. Graduate School of Information, Production and Systems, Waseda University Hibikino 2-7, Wakamatsu-ku, Kitakyushu, Fukuoka, 808-0135, Japan
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  • Kotaro Hirasawa

    Member, Corresponding author
    1. Graduate School of Information, Production and Systems, Waseda University Hibikino 2-7, Wakamatsu-ku, Kitakyushu, Fukuoka, 808-0135, Japan
    • Graduate School of Information, Production and Systems, Waseda University Hibikino 2-7, Wakamatsu-ku, Kitakyushu, Fukuoka, 808-0135, Japan
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Abstract

Many methods have been studied for mining association rules efficiently. However, because these methods usually generate a large number of rules, it is still a heavy burden for the users to find the most interesting ones. In this paper, we propose a novel method for finding what the user is interested in by assigning several keywords, like searching documents on the Web using search engines. By considering both the semantic similarity between the rules and keywords, and the statistical information like support, confidence, chi-squared value, etc. we could rank the rules by a new method named RuleRank, where evolutionary methods are applied to find the optimal ranking model. Experiments show that our approach is effective for the users to find what they want. Copyright © 2009 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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