Reducing gaps in quantitative association rules: A genetic programming free-parameter algorithm

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@Article{2014-ICAE-Gaps,
  author =       "Jose Maria Luna and Jose Raul Romero and 
                 Cristobal Romero and Sebastian Ventura",
  title =        "Reducing gaps in quantitative association rules: A
                 genetic programming free-parameter algorithm",
  journal =      "Integrated Computer-Aided Engineering",
  year =         "2014",
  volume =       "21",
  number =       "4",
  pages =        "321--337",
  month =        "29 " # sep,
  keywords =     "genetic algorithms, genetic programming, Quantitative
                 association rules, grammar guided genetic programming,
                 evolutionary computation, data mining",
  ISSN =         "1069-2509",
  publisher =    "IOS Press",
  DOI =          "doi:10.3233/ICA-140467",
  size =         "17 pages",
  abstract =     "The extraction of useful information for decision
                 making is a challenge in many different domains.
                 Association rule mining is one of the most important
                 techniques in this field, discovering relationships of
                 interest among patterns. Despite the mining of
                 association rules being an area of great interest for
                 many researchers, the search for well-grouped
                 continuous values is still a challenge, discovering
                 rules that do not comprise patterns which represent
                 unnecessary ranges of values. Existing algorithms for
                 mining association rules in continuous domains are
                 mainly based on a non-deterministic search, requiring a
                 high number of parameters to be optimised. These
                 parameters hinder the mining process, and the
                 algorithms themselves must be known to those data
                 mining experts that want to use them. We therefore
                 present a grammar guided genetic programming algorithm
                 that does not require as many parameters as other
                 existing approaches and enables the discovery of
                 quantitative association rules comprising small-size
                 gaps. The algorithm is verified over a varied set of
                 data, comparing the results to other association rule
                 mining algorithms from several paradigms. Additionally,
                 some resulting rules from different paradigms are
                 analysed, demonstrating the effectiveness of our model
                 for reducing gaps in numerical features.",
  notes =        "Department of Computer Science and Numerical Analysis,
                 University of Cordoba, Albert Einstein Building,
                 Rabanales Campus, Cordoba, Spain.

                 Department of Computer Science, Faculty of Computing
                 and Information Technology, King Abdulaziz University,
                 Saudi Arabia Kingdom",
}

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

Citations