Association Rule Mining with Chi-Squared Test Using Alternate Genetic Network Programming

  • Kaoru Shimada
  • Kotaro Hirasawa
  • Jinglu Hu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4065)


A method of association rule mining using Alternate Genetic Network Programming (aGNP) is proposed. GNP is one of the evolutionary optimization techniques, which uses directed graph structures as genes. aGNP is an extended GNP in terms of including two kinds of sets of node functions. The proposed system can extract important association rules whose antecedent and consequent are composed of the attributes of each family defined by users. The method measures the significance of association via chi-squared test using GNP’s features. Rule extraction is done without identifying frequent itemsets used in Apriori-like methods. Therefore, the method can be applied to rule extraction from dense database, and can extract dependent pairs of the sets of attributes in the database. Extracted rules are stored in a pool all together through generations and reflected in genetic operators as acquired information. In this paper, we describe the algorithm capable of finding the important association rules and present some experimental results.


Association Rule Genetic Operator Frequent Itemsets Node Function Association Rule Mining 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kaoru Shimada
    • 1
  • Kotaro Hirasawa
    • 1
  • Jinglu Hu
    • 1
  1. 1.Graduate School of Information, Production and SystemsWaseda UniversityFukuokaJapan

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