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JACIII Vol.10 No.1 pp. 102-111
doi: 10.20965/jaciii.2006.p0102
(2006)

Paper:

Genetic Network Programming with Acquisition Mechanisms of Association Rules

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:
June 8, 2005
Accepted:
October 18, 2005
Published:
January 20, 2006
Keywords:
evolutionary computation, genetic network programming, data mining, association rules
Abstract

A method of association rule mining using Genetic Network Programming (GNP) is proposed to improve the performance of association rule extraction. The proposed mechanisms can calculate measurements of association rules directly using GNP, and measure the significance of the association via the chi-squared test. Users can define the conditions of importance of association rules flexibly, which include the chi-squared value and the number of attributes in a rule. The proposed system evolves itself by an evolutionary method and obtains candidates of association rules by genetic operations. Extracted association rules are stored in a pool all together through generations and reflected in genetic operators as acquired information. Besides, our method can contain negation of attributes in association rules and suit association rule mining from dense databases. In this paper, we describe an extended algorithm capable of finding important association rules using GNP with sophisticated rule acquisition mechanisms and present some experimental results.

Cite this article as:
Kaoru Shimada, Kotaro Hirasawa, and Jinglu Hu, “Genetic Network Programming with Acquisition Mechanisms of Association Rules,” J. Adv. Comput. Intell. Intell. Inform., Vol.10, No.1, pp. 102-111, 2006.
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Last updated on Sep. 21, 2021