Time Related Class Association Rule Mining and Its Application to Traffic Prediction
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概要
- 論文の詳細を見る
In this paper, an algorithm capable of finding important time related association rules is proposed where Genetic Network Programming (GNP) with Attribute Accumulation Mechanism (AAM) and Extraction Mechanism at Stages (EMS) is used. Then, the classification system based on extracted time related association rules is proposed to estimate to which class the current traffic data belong. Using this kind of classification mechanism, the traffic prediction is available since the rules extracted are based on time sequences. And, we also present the experimental results on the traffic prediction problem.
著者
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MABU Shingo
Graduate School of Information, Production and Systems, Waseda University
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HIRASAWA Kotaro
Graduate School of Information, Production and Systems, Waseda University
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SHIMADA Kaoru
Graduate school of Information, Production and Systems, Waseda University
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ZHOU Huiyu
Graduate School of Information, Production and Systems, Waseda University
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WEI Wei
Graduate School of Information, Production and Systems, Waseda University
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