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Huiyu Zhou,Shingo Mabu,Manoj Kanta Mainali,Xianneng Li,Kaoru Shimada,Kotaro Hirasawa 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
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.
Yang Wang,Shingo Mabu,Huiyu Zhou,Xianneng Li,Kaoru Shimada,Bofeng Zhang,Kotaro Hirasawa 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
In this paper, a method of time-related classas sociation rule mining is proposed based on Genetic Network Programming(GNP) combined with Estimation of Distribution Algorithms(EDAs). The reare two important points in this paper: The first important point is to combine GNP with Estimation of Distribution Algorithms which are a novel evolution strategy. The second important point is that three kinds of probability models have been put for ward for generating new individuals. The aim of this paper is to extract more interesting association rules and to improve the traffic prediction accuracy by combining Genetic Network Proramming with Estimation of Distribution Algorithms. We applied the proposed data mining algorithm to traffic system sin order to predict the traffic volume in future. The simulation results show that our proposed method is effective compared with the conventional method based on GNP.