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

2006 ◽  
Author(s):  
Kaoru Shimada ◽  
Kotaro Hirasawa ◽  
Jinglu Hu
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Genetic Network ◽  
Network Programming ◽  
Rule Mining ◽  
Chi Squared ◽  
Class Association Rule ◽  
Genetic Network Programming

scholarly journals Class Association Rule Mining from Incomplete Database Using Genetic Network Programming

2008 ◽  
Vol 128 (5) ◽  
pp. 795-803 ◽  
Author(s):  
Kaoru Shimada ◽  
Shingo Mabu ◽  
Eiji Morikawa ◽  
Kotaro Hirasawa ◽  
Takayuki Furuzuki
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Genetic Network ◽  
Network Programming ◽  
Rule Mining ◽  
Class Association Rule ◽  
Genetic Network Programming

Genetic Network Programming with Acquisition Mechanisms of Association Rules

2006 ◽  
Vol 10 (1) ◽  
pp. 102-111 ◽  
Author(s):  
Kaoru Shimada ◽  
◽  
Kotaro Hirasawa ◽  
Jinglu Hu
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Genetic Network ◽  
Network Programming ◽  
Genetic Operators ◽  
Rule Mining ◽  
Rule Acquisition ◽  
Chi Squared ◽  
Genetic Network Programming

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.

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