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A nonlinear model to rank association rules based on semantic similarity and genetic network programing
Author(s) -
Yang Guangfei,
Shimada Kaoru,
Mabu Shingo,
Hirasawa Kotaro
Publication year - 2009
Publication title -
ieej transactions on electrical and electronic engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 30
eISSN - 1931-4981
pISSN - 1931-4973
DOI - 10.1002/tee.20385
Subject(s) - computer science , similarity (geometry) , ranking (information retrieval) , data mining , rank (graph theory) , semantic similarity , association rule learning , information retrieval , search engine , genetic algorithm , value (mathematics) , machine learning , artificial intelligence , mathematics , combinatorics , image (mathematics)
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.