On the adaptability of G3PARM to the extraction of rare association rules

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@Article{Luna:2013:KAIS,
  author =       "J. M. Luna and J. R. Romero and S. Ventura",
  title =        "On the adaptability of {G3PARM} to the extraction of
                 rare association rules",
  journal =      "Knowledge and Information Systems",
  year =         "2014",
  volume =       "38",
  number =       "2",
  pages =        "391--418",
  month =        feb,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, Rare
                 association rules, Grammar-guided genetic programming,
                 Evolutionary computation",
  ISSN =         "0219-1377",
  URL =          "http://dx.doi.org/10.1007/s10115-012-0591-9",
  DOI =          "doi:10.1007/s10115-012-0591-9",
  language =     "English",
  size =         "28 pages",
  abstract =     "To date, association rule mining has mainly focused on
                 the discovery of frequent patterns. Nevertheless, it is
                 often interesting to focus on those that do not
                 frequently occur. Existing algorithms for mining this
                 kind of infrequent patterns are mainly based on
                 exhaustive search methods and can be applied only over
                 categorical domains. In a previous work, the use of
                 grammar-guided genetic programming for the discovery of
                 frequent association rules was introduced, showing that
                 this proposal was competitive in terms of scalability,
                 expressiveness, flexibility and the ability to restrict
                 the search space. The goal of this work is to
                 demonstrate that this proposal is also appropriate for
                 the discovery of rare association rules. This approach
                 allows one to obtain solutions within specified time
                 limits and does not require large amounts of memory, as
                 current algorithms do. It also provides mechanisms to
                 discard noise from the rare association rule set by
                 applying four different and specific fitness functions,
                 which are compared and studied in depth. Finally, this
                 approach is compared with other existing algorithms for
                 mining rare association rules, and an analysis of the
                 mined rules is performed. As a result, this approach
                 mines rare rules in a homogeneous and low execution
                 time. The experimental study shows that this proposal
                 obtains a small and accurate set of rules close to the
                 size specified by the data miner.",
  notes =        "Also known as \cite{014-KAIS-RARE}",
}

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

Citations