Class association rules mining with time series and its application to traffic prediction
Huiyu Zhou, Wei Wei, Manoj Kanta Mainali, Kaoru Shimada, Shingo Mabu, Kotaro Hirasawa · 2008
An algorithm capable of finding important time related association rules and its application to classification systems have been described in this paper. We firstly describe a method of class association rule mining using genetic network programming (GNP) with time series processing mechanism in order to find time related sequence rules. Secondly, the classification system is applied to estimate to which class the current traffic data belong based on extracted association rules. Using this kinds of classification mechanism, the traffic prediction could be done since the rules extracted are based on time sequences. And, we also present experimental results using the traffic prediction problem.