Generalized Time Related Sequential Association Rule Mining and Traffic Prediction

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@InProceedings{Zhou:2009:cec,
  author =       "Huiyu Zhou and Shingo Mabu and Kaoru Shimada and 
                 Kotaro Hirasawa",
  title =        "Generalized Time Related Sequential Association Rule
                 Mining and Traffic Prediction",
  booktitle =    "2009 IEEE Congress on Evolutionary Computation",
  year =         "2009",
  editor =       "Andy Tyrrell",
  pages =        "2654--2661",
  address =      "Trondheim, Norway",
  month =        "18-21 " # may,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  isbn13 =       "978-1-4244-2959-2",
  file =         "P045.pdf",
  DOI =          "doi:10.1109/CEC.2009.4983275",
  abstract =     "Time Related Association rule mining is a kind of
                 sequence pattern mining for sequential databases. In
                 this paper, we introduce a method of Generalized
                 Association Rule Mining using Genetic Network
                 Programming (GNP) with time series processing mechanism
                 in order to find time related sequential rules
                 efficiently. GNP represents solutions as directed graph
                 structures, thus has compact structure and implicit
                 memory function. The inherent features of GNP make it
                 possible for GNP to work well especially in dynamic
                 environments. GNP has been applied to generate time
                 related candidate association rules as a tool using the
                 database consisting of a large number of time related
                 attributes. The aim of this algorithm is to better
                 handle association rule extraction from the databases
                 in a variety of time-related applications, especially
                 in the traffic volume prediction problems. The
                 generalized algorithm which can find the important time
                 related association rules is described and experimental
                 results are presented considering a traffic prediction
                 problem.",
  keywords =     "genetic algorithms, genetic programming, genetic
                 network programming, directed graph structure, genetic
                 network programming, road network, sequence pattern
                 mining, sequential database, time related association
                 rule mining, time series processing mechanism, traffic
                 volume prediction problem, data mining, directed
                 graphs, road traffic, time series, traffic engineering
                 computing",
  notes =        "CEC 2009 - A joint meeting of the IEEE, the EPS and
                 the IET. IEEE Catalog Number: CFP09ICE-CDR. Also known
                 as \cite{4983275}",
}

Genetic Programming entries for Huiyu Zhou Shingo Mabu Kaoru Shimada Kotaro Hirasawa

Citations