Mining Association Rules from Databases with Continuous Attributes using Genetic Network Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Taboada:2007:cec,
  author =       "Karla Taboada and Eloy Gonzales and Kaoru Shimada and 
                 Shingo Mabu and Kotaro Hirasawa and Jinglu Hu",
  title =        "Mining Association Rules from Databases with
                 Continuous Attributes using Genetic Network
                 Programming",
  booktitle =    "2007 IEEE Congress on Evolutionary Computation",
  year =         "2007",
  editor =       "Dipti Srinivasan and Lipo Wang",
  pages =        "1311--1317",
  address =      "Singapore",
  month =        "25-28 " # sep,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  ISBN =         "1-4244-1340-0",
  file =         "1162.pdf",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/CEC.2007.4424622",
  abstract =     "Most association rule mining algorithms make use of
                 discretisation algorithms for handling continuous
                 attributes. Discretization is a process of transforming
                 a continuous attribute value into a finite number of
                 intervals and assigning each interval to a discrete
                 numerical value. However, by means of methods of
                 discretisation, it is difficult to get highest
                 attribute interdependency and at the same time to get
                 lowest number of intervals. In this paper we present an
                 association rule mining algorithm that is suited for
                 continuous valued attributes commonly found in
                 scientific and statistical databases. We propose a
                 method using a new graph-based evolutionary algorithm
                 named {"}Genetic Network Programming (GNP){"} that can
                 deal with continues values directly, that is, without
                 using any discretisation method as a preprocessing
                 step. GNP represents its individuals using graph
                 structures and evolve them in order to find a solution;
                 this feature contributes to creating quite compact
                 programs and implicitly memorising past action
                 sequences. In the proposed method using GNP, the
                 significance of the extracted association rule is
                 measured by the use of the chi-squared test and only
                 important association rules are stored in a pool all
                 together through generations. Results of experiments
                 conducted on a real life database suggest that the
                 proposed method provides an effective technique for
                 handling continuous attributes.",
  notes =        "CEC 2007 - A joint meeting of the IEEE, the EPS, and
                 the IET.

                 IEEE Catalog Number: 07TH8963C",
}

Genetic Programming entries for Karla Taboada Eloy Gonzales Kaoru Shimada Shingo Mabu Kotaro Hirasawa Jinglu Hu

Citations