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

Kaoru Shimada*, Kotaro Hirasawa, Jinglu Hu

*この研究の対応する著者

研究成果査読

5 被引用数 (Scopus)

抄録

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.

本文言語English
ページ(範囲)202-216
ページ数15
ジャーナルLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
4065 LNAI
DOI
出版ステータスPublished - 2006 1 1
イベント6th Industrial Conference on Data Mining, ICDM 2006 - Leipzig, Germany
継続期間: 2006 7 142006 7 15

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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