Mining exceptional relationships with grammar-guided genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{journals/kais/LunaPV16,
  author =       "Jose Maria Luna and Mykola Pechenizkiy and 
                 Sebastian Ventura",
  title =        "Mining exceptional relationships with grammar-guided
                 genetic programming",
  journal =      "Knowledge and Information Systems",
  year =         "2016",
  number =       "3",
  volume =       "47",
  pages =        "571--594",
  keywords =     "genetic algorithms, genetic programming, Association
                 rules, Exceptional subgroups",
  bibdate =      "2016-05-13",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/kais/kais47.html#LunaPV16",
  ISSN =         "0219-1377",
  URL =          "http://dx.doi.org/10.1007/s10115-015-0859-y",
  DOI =          "doi:10.1007/s10115-015-0859-y",
  abstract =     "Given a database of records, it might be possible to
                 identify small subsets of data which distribution is
                 exceptionally different from the distribution in the
                 complete set of data records. Finding such interesting
                 relationships, which we call exceptional relationships,
                 in an automated way would allow discovering unusual or
                 exceptional hidden behaviour. In this paper, we
                 formulate the problem of mining exceptional
                 relationships as a special case of exceptional model
                 mining and propose a grammar-guided genetic programming
                 algorithm (MERG3P) that enables the discovery of any
                 exceptional relationships. In particular, MERG3P can
                 work directly not only with categorical, but also with
                 numerical data. In the experimental evaluation, we
                 conduct a case study on mining exceptional relations
                 between well-known and widely used quality measures of
                 association rules, which exceptional behaviour would be
                 of interest to pattern mining experts. For this
                 purpose, we constructed a data set comprising a wide
                 range of values for each considered association rule
                 quality measure, such that possible exceptional
                 relations between measures could be discovered. Thus,
                 besides the actual validation of MERG3P, we found that
                 the Support and Leverage measures in fact are
                 negatively correlated under certain conditions, while
                 in general experts in the field expect these measures
                 to be positively correlated",
}

Genetic Programming entries for Jose Maria Luna Mykola Pechenizkiy Sebastian Ventura

Citations