Abstract
In this chapter, we introduce 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. This process is called Sequence Pattern Mining. 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. And, we also present experimental results using the traffic prediction problem. The aim of sequential pattern mining is to better handle association rule extraction of the databases in a variety of time-related applications, for example, in the traffic prediction problems. The algorithm which can find the important sequential association rules is described and several experimental results are presented considering a traffic prediction problem.
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Zhou, H., Shimada, K., Mabu, S., Hirasawa, K. (2009). Sequence Pattern Mining. In: Abraham, A., Hassanien, AE., de Carvalho, A.P.d.L.F. (eds) Foundations of Computational Intelligence Volume 4. Studies in Computational Intelligence, vol 204. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01088-0_2
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DOI: https://doi.org/10.1007/978-3-642-01088-0_2
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