Generalized rule extraction and traffic prediction in the optimal route search
Huiyu Zhou, Shingo Mabu, Xianneng Li, Kaoru Shimada, Kotaro Hirasawa · 2010
Time Related Association rule mining is a kind of sequence pattern mining for sequential databases. In this paper, a method of Generalized Association Rule Mining using Genetic Network Programming (GNP) with MBFP(Multi-Branch and Full-Pathes) processing mechanism has been introduced in order to find time related sequential rules more efficiently. GNP represents solutions as directed graph structures, thus has compact structure and partially observable Markov decision process. GNP has been applied to generate time related candidate association rules as a tool using the database consisting of a large number of time related attributes. The aim of this algorithm is to better handle association rule extraction from the databases in a variety of time-related applications, especially in the traffic volume prediction and its usage. The generalized algorithm which can find the important time related association rules has been proposed and experimental results are presented considering how to use the rules to predict the future traffic volume and also how to use the traffic prediction in the optimal search problem.