An Evolutionary Algorithm for the Discovery of Rare Class Association Rules in Learning Management Systems

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@Article{2014-AI-Luna,
  author =       "J. M. Luna and C. Romero and J. R. Romero and 
                 S. Ventura",
  title =        "An Evolutionary Algorithm for the Discovery of Rare
                 Class Association Rules in Learning Management
                 Systems",
  journal =      "Applied Intelligence",
  year =         "2015",
  volume =       "42",
  number =       "3",
  pages =        "501--513",
  month =        apr,
  keywords =     "genetic algorithms, genetic programming, Rare
                 association rules, Grammar guided genetic programming,
                 Evolutionary computation, Educational data mining",
  publisher =    "Springer US",
  language =     "English",
  ISSN =         "0924-669X",
  URL =          "http://dx.doi.org/10.1007/s10489-014-0603-4",
  DOI =          "doi:10.1007/s10489-014-0603-4",
  size =         "13 pages",
  abstract =     "Association rule mining, an important data mining
                 technique, has been widely focused on the extraction of
                 frequent patterns. Nevertheless, in some application
                 domains it is interesting to discover patterns that do
                 not frequently occur, even when they are strongly
                 related. More specifically, this type of relation can
                 be very appropriate in e-learning domains due to its
                 intrinsic imbalanced nature. In these domains, the aim
                 is to discover a small but interesting and useful set
                 of rules that could barely be extracted by traditional
                 algorithms founded in exhaustive search-based
                 techniques. In this paper, we propose an evolutionary
                 algorithm for mining rare class association rules when
                 gathering student usage data from a Moodle system. We
                 analyse how the use of different parameters of the
                 algorithm determine the rule characteristics, and
                 provides some illustrative examples of them to show
                 their interpretability and usefulness in e-learning
                 environments. We also compare our approach to other
                 existing algorithms for mining both rare and frequent
                 association rules. Finally, an analysis of the rules
                 mined is presented, which allows information about
                 students' unusual behaviour regarding the achievement
                 of bad or good marks to be discovered.",
}

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

Citations