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- W1973417042 abstract "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." @default.
- W1973417042 created "2016-06-24" @default.
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- W1973417042 date "2010-01-01" @default.
- W1973417042 modified "2023-09-24" @default.
- W1973417042 title "Time Related Class Association Rule Mining and Its Application to Traffic Prediction" @default.
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- W1973417042 doi "https://doi.org/10.1541/ieejeiss.130.289" @default.
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