Mining Context-Aware Association Rules Using Grammar-Based Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

  author =       "Jose Maria Luna and Mykola Pechenizkiy and 
                 Maria Jose {del Jesus} and Sebastian Ventura",
  title =        "Mining Context-Aware Association Rules Using
                 Grammar-Based Genetic Programming",
  journal =      "IEEE Transactions on Cybernetics",
  year =         "2018",
  keywords =     "genetic algorithms, genetic programming, Association
                 rules, context awareness, contextual features",
  publisher =    "IEEE",
  ISSN =         "2168-2267",
  DOI =          "doi:10.1109/TCYB.2017.2750919",
  size =         "15 pages",
  abstract =     "Real-world data usually comprise features whose
                 interpretation depends on some contextual information.
                 Such contextual-sensitive features and patterns are of
                 high interest to be discovered and analysed in order to
                 obtain the right meaning. This paper formulates the
                 problem of mining context-aware association rules,
                 which refers to the search for associations between
                 itemsets such that the strength of their implication
                 depends on a contextual feature. For the discovery of
                 this type of associations, a model that restricts the
                 search space and includes syntax constraints by means
                 of a grammar-based genetic programming methodology is
                 proposed. Grammars can be considered as a useful way of
                 introducing subjective knowledge to the pattern mining
                 process as they are highly related to the background
                 knowledge of the user. The performance and usefulness
                 of the proposed approach is examined by considering
                 synthetically generated datasets. A posteriori analysis
                 on different domains is also carried out to demonstrate
                 the utility of this kind of associations. For example,
                 in educational domains, it is essential to identify and
                 understand contextual and context-sensitive factors
                 that affect overall and individual student behaviour
                 and performance. The results of the experiments suggest
                 that the approach is feasible and it automatically
                 identifies interesting context-aware associations from
                 real-world datasets.",
  notes =        "PubMed ID: 28952954 also known as \cite{8049471}",

Genetic Programming entries for Jose Maria Luna Mykola Pechenizkiy Maria Jose del Jesus Sebastian Ventura