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Backward time related association rule mining in trafficprediction using genetic network programming withdatabase rearrangement

Published:08 July 2009Publication History

ABSTRACT

In this paper, we introduce Backward Time Related Association Rule Mining using Genetic Network Programming (GNP) with Database Rearrangement in order to find time related sequential association from time related databases effectively and efficiently. The proposed algorithm and experimental results are described using a traffic prediction problem.

References

  1. S. Mabu, K. Hirasawa and J. Hu, "A Graph-Based Evolutionary Algorithm: Genetic Network Programming(GNP) and Its Extension Using Reinforcement Learning", Evolutionary Computation, MIT press, Vol. 15, No.3, pp. 369--398, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. T. Eguchi, K. Hirasawa, J. Hu and N. Ota, "A study of Evolutionary Multiagent Models Based on Symbiosis", IEEE Trans. on Syst., Man and Cybernetics -- Part B --, Vol.36, No.1, pp.179--193, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. K. Hirasawa, T. Eguchi, J. Zhou, L. Yu and S. Markon, A Double-Deck Elevator Group Supervisory Control System Using Genetic Network Programming, IEEE Trans. on Systems, Man and Cybernetics, Part C, Vol. 38, No. 4, pp. 535--550, 2008/7. Google ScholarGoogle ScholarDigital LibraryDigital Library

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  1. Backward time related association rule mining in trafficprediction using genetic network programming withdatabase rearrangement

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