Time Related Association Rules Mining with Attributes Accumulation Mechanism and its Application to Traffic Prediction
Huiyu Zhou, Wei Wei, Kaoru Shimada, Shingo Mabu, and Kotaro Hirasawa
Graduate School of Information, Production and Systems, Waseda University
Hibikino 2-7, Wakamatsu-ku, Kitakyushu, Fukuoka 808-0135, Japan,
In this paper, we propose a method of association rule mining using Genetic Network Programming (GNP) with time series processing mechanism and attributes accumulation mechanism in order to find time related sequence rules efficiently in association rule extraction systems. GNP, a kind of evolutionary computation, represents solutions using graph structures. Because of the inherent features of GNP, it works well in dynamic environments. In this paper, GNP is applied to generate candidate association rules using the database consisting of a large number of time related attributes. In order to deal with a large number of attributes, GNP individual accumulates fitter attributes gradually during rounds, and the rules of each round are stored in a Small Rule Pool using a hash method, then, the rules are finally stored in a Big Rule Pool after the check of the overlap at the end of each round. The aim of this paper is to better handle association rule extraction of the databases in a variety of time-related applications, especially in the traffic prediction problems. The algorithm which can find the important time related association rules is described and several experimental results are presented considering a traffic prediction problem.
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