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

Kaoru Shimada, Kotaro Hirasawa, Takayuki Furuzuki

Research output: Contribution to journalArticle

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

Fingerprint

Network Programming
Genetic Network
Chi-squared test
Association Rule Mining
Association rules
Genetic Programming
Alternate
Rule Extraction
Association Rules
Attribute
Directed graphs
Databases
Frequent Itemsets
Genetic Operators
Evolutionary Optimization
Genes
Directed Graph
Optimization Techniques
Gene
Dependent

ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

@article{1484628cf6274381a26d68a95a40d3c4,
title = "Association rule mining with chi-squared test using alternate genetic network programming",
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.",
author = "Kaoru Shimada and Kotaro Hirasawa and Takayuki Furuzuki",
year = "2006",
doi = "10.1007/11790853_16",
language = "English",
volume = "4065 LNAI",
pages = "202--216",
journal = "Lecture Notes in Computer Science",
issn = "0302-9743",
publisher = "Springer Verlag",

}

TY - JOUR

T1 - Association rule mining with chi-squared test using alternate genetic network programming

AU - Shimada, Kaoru

AU - Hirasawa, Kotaro

AU - Furuzuki, Takayuki

PY - 2006

Y1 - 2006

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=33746404233&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33746404233&partnerID=8YFLogxK

U2 - 10.1007/11790853_16

DO - 10.1007/11790853_16

M3 - Article

AN - SCOPUS:33746404233

VL - 4065 LNAI

SP - 202

EP - 216

JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

SN - 0302-9743

ER -