Association rule mining with chi-squared test using alternate genetic network programming

Kaoru Shimada, Kotaro Hirasawa, Jinglu Hu

Research output: Contribution to journalConference article

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)202-216
Number of pages15
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4065 LNAI
DOIs
Publication statusPublished - 2006 Jan 1
Event6th Industrial Conference on Data Mining, ICDM 2006 - Leipzig, Germany
Duration: 2006 Jul 142006 Jul 15

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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