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

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

^*Corresponding author for this work

Information, Production and Systems Research Center

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings
Pages	147-152
Number of pages	6
Publication status	Published - 2009
Event	ICROS-SICE International Joint Conference 2009, ICCAS-SICE 2009 - Fukuoka Duration: 2009 Aug 18 → 2009 Aug 21

Other

Other	ICROS-SICE International Joint Conference 2009, ICCAS-SICE 2009
City	Fukuoka
Period	09/8/18 → 09/8/21

ASJC Scopus subject areas

Information Systems
Control and Systems Engineering
Industrial and Manufacturing Engineering

Cite this

Generalized association rules mining with multi-branches• full-paths and its application to traffic volume prediction. / Zhou, Huiyu; Mabu, Shingo; Mainali, Manoj Kanta et al.

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

Research output: Chapter in Book/Report/Conference proceeding › Conference 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.

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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.",

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AU - Zhou, Huiyu

AU - Mabu, Shingo

AU - Mainali, Manoj Kanta

AU - Li, Xianneng

AU - Shimada, Kaoru

AU - Hirasawa, Kotaro

PY - 2009

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AB - 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.

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

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ASJC Scopus subject areas

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