Analysis of the Effectiveness of G3PARM Algorithm

  • J. M. Luna
  • J. R. Romero
  • S. Ventura
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6077)


This paper presents an evolutionary algorithm using G3P (Grammar Guided Genetic Programming) for mining association rules in different real-world databases. This algorithm, called G3PARM, uses an auxiliary population made up of its best individuals that will then act as parents for the next generation. The individuals are defined through a context-free grammar and it allows us to obtain datatype-generic and valid individuals. We compare our approach to Apriori and FP-Growth algorithms and demonstrate that our proposal obtains rules with better support, confidence and coverage of the dataset instances. Finally, a preliminary study is also introduced to compare the scalability of our algorithm. Our experimental studies illustrate that this approach is highly promising for discovering association rules in databases.


Genetic Programming Association Rules G3P 


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  1. 1.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: VLDB 1994, Proceedings of 20th International Conference on Very Large Data Bases, Santiago de Chile, Chile, September 1994, pp. 487–499 (1994)Google Scholar
  2. 2.
    Borgelt, C.: Efficient implementations of Apriori and Eclat. In: FIMI 2003, 1st Workshop on Frequent Itemset Mining Implementations, Melbourne, Florida, USA (December 2003)Google Scholar
  3. 3.
    Coenen, F., Goulbourne, G., Leng, P.: Tree structures for mining association rules. Data Mining and Knowledge Discovery 8(1), 25–51 (2003)CrossRefMathSciNetGoogle Scholar
  4. 4.
    Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer, New York (2003)zbMATHGoogle Scholar
  5. 5.
    Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, pp. 1–12 (2000)Google Scholar
  6. 6.
    Papè, N.F., Alcalá-Fdez, J., Bonarini, A., Herrera, F.: Evolutionary extraction of association rules: A preliminary study on their effectiveness. In: Corchado, E., Wu, X., Oja, E., Herrero, Á., Baruque, B. (eds.) HAIS 2009. LNCS, vol. 5572, pp. 646–653. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  7. 7.
    Tsymbal, A., Pechenizkiy, M., Cunningham, P.: Sequential genetic search for ensemble feature selection. In: Nineteenth International Joint Conference on Artificial Intelligence (IJCAI 2005), Edinburgh, Scotland, August 2005, pp. 877–882 (2005)Google Scholar
  8. 8.
    Wei, Z., Hongzhi, L., Na, Z.: Research on the fp growth algorithm about association rule mining. In: ICFCC 2009, International Conference on Future Computer and Communication, Kuala Lumpur, Malaysia, April 2009, pp. 572–576 (2009)Google Scholar
  9. 9.
    Yang, G., Shimada, K., Mabu, S., Hirasawa, K.: A nonlinear model to rank association rules based on semantic similarity and genetic network programming, vol. 4, pp. 248–256. Institute of Electrical Engineers of Japan (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • J. M. Luna
    • 1
  • J. R. Romero
    • 1
  • S. Ventura
    • 1
  1. 1.Dept. of Computer Science and Numerical AnalysisUniversity of CórdobaCórdobaSpain

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