A Personalized Association Rule Ranking Method Based on Semantic Similarity and Evolutionary Computation

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@InProceedings{Yang3:2008:cec,
  author =       "Guangfei Yang and Kaoru Shimada and Shingo Mabu and 
                 Kotaro Hirasawa",
  title =        "A Personalized Association Rule Ranking Method Based
                 on Semantic Similarity and Evolutionary Computation",
  booktitle =    "2008 IEEE World Congress on Computational
                 Intelligence",
  year =         "2008",
  editor =       "Jun Wang",
  pages =        "487--494",
  address =      "Hong Kong",
  month =        "1-6 " # jun,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  isbn13 =       "978-1-4244-1823-7",
  file =         "EC0132.pdf",
  DOI =          "doi:10.1109/CEC.2008.4630842",
  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 WWW
                 by search engines. We build an ontology to describe the
                 concepts and relationships in the research domain and
                 mine association rules by Genetic Network Programming
                 from the database where the attributes are concepts in
                 ontology. By considering both the semantic similarity
                 between the rules and the keywords, and the statistical
                 information like support, confidence, chi-squared
                 value, we could rank the rules by a new method named
                 RuleRank, where genetic algorithm is applied to adjust
                 the parameters and the optimal ranking model is built
                 for the user. Experiments show that our approach is
                 effective for the users to find what they want.",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "WCCI 2008 - A joint meeting of the IEEE, the INNS, the
                 EPS and the IET.",
}

Genetic Programming entries for Guangfei Yang Kaoru Shimada Shingo Mabu Kotaro Hirasawa

Citations