Journal of Japan Society for Fuzzy Theory and Intelligent Informatics
Online ISSN : 1881-7203
Print ISSN : 1347-7986
ISSN-L : 1347-7986
Original Papers
Association Rule Mining Using Genetic Network Programming
Kaoru SHIMADAKotaro HIRASAWATakayuki FURUZUKI
Author information
JOURNALS FREE ACCESS

2006 Volume 18 Issue 6 Pages 881-891

Details
Abstract

A method of association rule mining is proposed using Genetic Network Programming (GNP) to improve the performance of rule extraction. Association rule mining is the discovery of association relationships or correlations among a set of attributes in a database. GNP is one of the evolutionary optimization techniques, which uses directed graph structures as genes. GNP examines the attribute values of database tuples using judgement nodes and calculates the measurements of association rules using processing nodes. In addition, the proposed method measures the significance of associations via the chi-squared test for correlation used in classical statistics using GNP's feature. Extracted association rules are stored in a pool all together through the generations in order to find new important rules. Therefore, the proposed method is fundamentally different from the previous methods in its evolutionary way. In this paper, the algorithm capable of finding the important association rules is described and some experimental results are shown.

Information related to the author
© 2006 Japan Society for Fuzzy Theory and Intelligent Informatics
Previous article Next article
feedback
Top