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JACIII Vol.10 No.6 pp. 954-963
doi: 10.20965/jaciii.2006.p0954
(2006)

Paper:

Alternate Genetic Network Programming with Association Rules Acquisition Mechanisms Between Attribute Families

Kaoru Shimada, Kotaro Hirasawa, and Jinglu Hu

Graduate School of Information, Production and Systems, Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu-shi, Fukuoka 808-0135, Japan

Received:
February 17, 2006
Accepted:
April 14, 2006
Published:
November 20, 2006
Keywords:
evolutionary computation, genetic network programming, data mining, association rules
Abstract

A method of association rule mining with chi-squared test 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 node function sets. The proposed system can extract important association rules whose antecedent and consequent are composed of the attributes of each family defined by users. Rule extraction is done without identifying frequent itemsets used in Apriori-like methods. 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.

Cite this article as:
Kaoru Shimada, Kotaro Hirasawa, and Jinglu Hu, “Alternate Genetic Network Programming with Association Rules Acquisition Mechanisms Between Attribute Families,” J. Adv. Comput. Intell. Intell. Inform., Vol.10, No.6, pp. 954-963, 2006.
Data files:
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