• DocumentCode
    2333835
  • Title

    G3PARM: A Grammar Guided Genetic Programming algorithm for mining association rules

  • Author

    Luna, José María ; Romero, José Raúl ; Ventura, Sebastián

  • Author_Institution
    Dept. of Comput. Sci. & Numerical Anal., Univ. of Cordoba, Córdoba, Spain
  • fYear
    2010
  • fDate
    18-23 July 2010
  • Firstpage
    1
  • Lastpage
    8
  • Abstract
    This paper presents the G3PARM algorithm for mining representative association rules. G3PARM is an evolutionary algorithm that uses G3P (Grammar Guided Genetic Programming) and an auxiliary population made up of its best individuals who will then act as parents for the next generation. Due to the nature of G3P, the G3PARM algorithm allows us to obtain valid individuals by defining them through a context-free grammar and, furthermore, this algorithm is generic with respect to data type. We compare our algorithm to two multiobjective algorithms frequently used in literature and known as NSGA2 (Non dominated Sort Genetic Algorithm) and SPEA2 (Strength Pareto Evolutionary Algorithm) and demonstrate the efficiency of our algorithm in terms of running-time, coverage and average support, providing the user with high representative rules.
  • Keywords
    context-free grammars; data mining; genetic algorithms; association rules mining; auxiliary population; context-free grammar; evolutionary algorithm; grammar guided genetic programming algorithm; Association rules; Databases; Encoding; Evolutionary computation; Genetic programming; Grammar;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Evolutionary Computation (CEC), 2010 IEEE Congress on
  • Conference_Location
    Barcelona
  • Print_ISBN
    978-1-4244-6909-3
  • Type

    conf

  • DOI
    10.1109/CEC.2010.5586504
  • Filename
    5586504