Comparative Association Rules Mining Using Genetic Network Programming (GNP) with Attributes Accumulation Mechanism and its Application to Traffic Systems

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@InProceedings{Wei:2008:cec,
  author =       "Wei Wei and Huiyu Zhou and Kaoru Shimada and 
                 Shingo Mabu and Kotaro Hirasawa",
  title =        "Comparative Association Rules Mining Using Genetic
                 Network Programming (GNP) with Attributes Accumulation
                 Mechanism and its Application to Traffic Systems",
  booktitle =    "2008 IEEE World Congress on Computational
                 Intelligence",
  year =         "2008",
  editor =       "Jun Wang",
  pages =        "292--298",
  address =      "Hong Kong",
  month =        "1-6 " # jun,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  isbn13 =       "978-1-4244-1823-7",
  file =         "EC0090.pdf",
  DOI =          "doi:10.1109/CEC.2008.4630813",
  abstract =     "In this paper, we present a method of comparative
                 association rules mining using Genetic Network
                 Programming (GNP) with attributes accumulation
                 mechanism in order to uncover association rules between
                 different datasets. GNP is an evolutionary approach
                 which can evolve itself and find the optimal solutions.
                 The motivation of the comparative association rules
                 mining method is to use the data mining approach to
                 check two or more databases instead of one, so as to
                 find the hidden relations among them. The proposed
                 method measures the importance of association rules by
                 using the absolute difference of confidences among
                 different databases and can get a number of interesting
                 rules. Association rules obtained by comparison can
                 help us to find and analyse the explicit and implicit
                 patterns among a large amount of data. For the large
                 attributes case, the calculation is very
                 time-consuming, when the conventional GNP based data
                 mining is used. So, we have proposed an attribute
                 accumulation mechanism to improve the performance.
                 Then, the comparative association rules mining using
                 GNP has been applied to a complicated traffic system.
                 By mining and analysing the rules under different
                 traffic situations, it was found that we can get
                 interesting information of the traffic system.",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "WCCI 2008 - A joint meeting of the IEEE, the INNS, the
                 EPS and the IET.",
}

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

Citations