Design and behavior study of a grammar-guided genetic programming algorithm for mining association rules

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@Article{journals/kais/LunaRV12,
  author =       "Jose Maria Luna and Jose Raul Romero and 
                 Sebastian Ventura",
  title =        "Design and behavior study of a grammar-guided genetic
                 programming algorithm for mining association rules",
  journal =      "Knowledge and Information Systems",
  year =         "2012",
  number =       "1",
  volume =       "32",
  pages =        "53--76",
  month =        jul,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, association
                 rules, grammar-guided genetic programming, evolutionary
                 algorithms",
  language =     "English",
  ISSN =         "0219-1377",
  DOI =          "doi:10.1007/s10115-011-0419-z",
  size =         "24 pages",
  abstract =     "This paper presents a proposal for the extraction of
                 association rules called G3PARM (Grammar-Guided Genetic
                 Programming for Association Rule Mining) that makes the
                 knowledge extracted more expressive and flexible. This
                 algorithm allows a context-free grammar to be adapted
                 and applied to each specific problem or domain and
                 eliminates the problems raised by discretisation. This
                 proposal keeps the best individuals (those that exceed
                 a certain threshold of support and confidence) obtained
                 with the passing of generations in an auxiliary
                 population of fixed size n . G3PARM obtains solutions
                 within specified time limits and does not require the
                 large amounts of memory that the exhaustive search
                 algorithms in the field of association rules do. Our
                 approach is compared to exhaustive search (Apriori and
                 FP-Growth) and genetic (QuantMiner and ARMGA)
                 algorithms for mining association rules and performs an
                 analysis of the mined rules. Finally, a series of
                 experiments serve to contrast the scalability of our
                 algorithm. The proposal obtains a small set of rules
                 with high support and confidence, over 90 and 99percent
                 respectively. Moreover, the resulting set of rules
                 closely satisfies all the dataset instances. These
                 results illustrate that our proposal is highly
                 promising for the discovery of association rules in
                 different types of datasets.",
  affiliation =  "Department of Computer Science and Numerical Analysis,
                 University of Cordoba, Rabanales Campus, 14071 Cordoba,
                 Spain",
  bibdate =      "2012-07-03",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/kais/kais32.html#LunaRV12",
}

Genetic Programming entries for Jose Maria Luna Jose Raul Romero Salguero Sebastian Ventura

Citations