Generalized association rules mining with multi-branches• full-paths and its application to traffic volume prediction

Huiyu Zhou, Shingo Mabu, Manoj Kanta Mainali, Xianneng Li, Kaoru Shimada, Kotaro Hirasawa

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

1 Citation (Scopus)

Abstract

Time Related Association rule mining is a kind of sequence pattern mining for sequential databases. In this paper, a Generalized Class Association Rule Mining is proposed using Genetic Network Programming (GNP) in order to find time related sequential rules more efficiently. GNP has been applied to generate the candidates of the time related association rules as a tool. For fully utilizing the potential ability of GNP structure, the mechanism of Generalized GNP with Multi-Branches• Full-Paths mechanism is proposed for class association data mining. The aim of this algorithm is to better handle association rule extraction from the databases with high efficiency in a variety of time-related applications, especially in the traffic volume prediction problems. The algorithm capable of finding the important time related association rules is described and experimental results are presented using a traffic prediction problem.

Original languageEnglish
Title of host publicationICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings
Pages147-152
Number of pages6
Publication statusPublished - 2009
EventICROS-SICE International Joint Conference 2009, ICCAS-SICE 2009 - Fukuoka
Duration: 2009 Aug 182009 Aug 21

Other

OtherICROS-SICE International Joint Conference 2009, ICCAS-SICE 2009
CityFukuoka
Period09/8/1809/8/21

Fingerprint

Association rules
Data mining

ASJC Scopus subject areas

  • Information Systems
  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering

Cite this

Zhou, H., Mabu, S., Mainali, M. K., Li, X., Shimada, K., & Hirasawa, K. (2009). Generalized association rules mining with multi-branches• full-paths and its application to traffic volume prediction. In ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings (pp. 147-152). [5333340]

Generalized association rules mining with multi-branches• full-paths and its application to traffic volume prediction. / Zhou, Huiyu; Mabu, Shingo; Mainali, Manoj Kanta; Li, Xianneng; Shimada, Kaoru; Hirasawa, Kotaro.

ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings. 2009. p. 147-152 5333340.

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

Zhou, H, Mabu, S, Mainali, MK, Li, X, Shimada, K & Hirasawa, K 2009, Generalized association rules mining with multi-branches• full-paths and its application to traffic volume prediction. in ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings., 5333340, pp. 147-152, ICROS-SICE International Joint Conference 2009, ICCAS-SICE 2009, Fukuoka, 09/8/18.
Zhou H, Mabu S, Mainali MK, Li X, Shimada K, Hirasawa K. Generalized association rules mining with multi-branches• full-paths and its application to traffic volume prediction. In ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings. 2009. p. 147-152. 5333340
Zhou, Huiyu ; Mabu, Shingo ; Mainali, Manoj Kanta ; Li, Xianneng ; Shimada, Kaoru ; Hirasawa, Kotaro. / Generalized association rules mining with multi-branches• full-paths and its application to traffic volume prediction. ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings. 2009. pp. 147-152
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