Class association rule mining with chi-squared test using Genetic Network Programming

Kaoru Shimada, Kotaro Hirasawa, Jinglu Hu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

60 Citations (Scopus)

Abstract

An efficient algorithm for important class association rule mining using Genetic Network Programming (GNP) is proposed. GNP is one of the evolutionary optimization techniques, which uses directed graph structures as genes. Instead of generating a large number of candidate rules, the method can obtain a sufficient number of important association rules for classification. The proposed method measures the significance of the association via the chi-squared test. Therefore, all the extracted important rules can be used for classification directly. In addition, the method suits class association rule mining from dense databases, where many frequently occurring items are found in each tuple. Users can define conditions of extracting important class association rules. In this paper, we describe an algorithm for class association rule mining with chi-squared test using GNP and present a classifier using these extracted rules.

Original languageEnglish
Title of host publication2006 IEEE International Conference on Systems, Man and Cybernetics
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5338-5344
Number of pages7
ISBN (Print)1424401003, 9781424401000
DOIs
Publication statusPublished - 2006 Jan 1
Event2006 IEEE International Conference on Systems, Man and Cybernetics - Taipei, Taiwan, Province of China
Duration: 2006 Oct 82006 Oct 11

Publication series

NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Volume6
ISSN (Print)1062-922X

Conference

Conference2006 IEEE International Conference on Systems, Man and Cybernetics
CountryTaiwan, Province of China
CityTaipei
Period06/10/806/10/11

ASJC Scopus subject areas

  • Engineering(all)

Fingerprint Dive into the research topics of 'Class association rule mining with chi-squared test using Genetic Network Programming'. Together they form a unique fingerprint.

  • Cite this

    Shimada, K., Hirasawa, K., & Hu, J. (2006). Class association rule mining with chi-squared test using Genetic Network Programming. In 2006 IEEE International Conference on Systems, Man and Cybernetics (pp. 5338-5344). [4274766] (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics; Vol. 6). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICSMC.2006.385157