Grammar-based multi-objective algorithms for mining association rules

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  title =        "Grammar-based multi-objective algorithms for mining
                 association rules",
  author =       "J. M. Luna and J. R. Romero and S. Ventura",
  journal =      "Data \& Knowledge Engineering",
  year =         "2013",
  volume =       "86",
  pages =        "19--37",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming, Association
                 rule mining, Data mining, Mining methods and
  ISSN =         "0169-023X",
  DOI =          "doi:10.1016/j.datak.2013.01.002",
  size =         "19 pages",
  abstract =     "In association rule mining, the process of extracting
                 relations from a dataset often requires the application
                 of more than one quality measure and, in many cases,
                 such measures involve conflicting objectives. In such a
                 situation, it is more appropriate to attain the optimal
                 trade-off between measures. This paper deals with the
                 association rule mining problem under a multi-objective
                 perspective by proposing grammar guided genetic
                 programming (G3P) models, that enable the extraction of
                 both numerical and nominal association rules in only
                 one single step. The strength of G3P is its ability to
                 restrict the search space and build rules conforming to
                 a given context-free grammar. Thus, the proposals
                 presented in this paper combine the advantages of G3P
                 models with those of multi-objective approaches. Both
                 approaches follow the philosophy of two well-known
                 multi-objective algorithms: the Non-dominated Sort
                 Genetic Algorithm (NSGA-2) and the Strength Pareto
                 Evolutionary Algorithm (SPEA-2).

                 In the experimental stage, we compare both
                 multi-objective algorithms to a single-objective G3P
                 proposal for mining association rules and perform an
                 analysis of the mined rules. The results obtained show
                 that multi-objective proposals obtain very frequent
                 (with support values above 95percent in most cases) and
                 reliable (with confidence values close to 100percent)
                 rules when attaining the optimal trade-off between
                 support and confidence. Furthermore, for the trade-off
                 between support and lift, the multi-objective proposals
                 also produce very interesting and representative

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