JACIII Vol.10 No.1 pp. 102-111
doi: 10.20965/jaciii.2006.p0102


Genetic Network Programming with Acquisition Mechanisms of Association Rules

Kaoru Shimada, Kotaro Hirasawa, and Jinglu Hu

Graduate School of Information, Production and Systems, Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu-shi, Fukuoka 808-0135, Japan

June 8, 2005
October 18, 2005
January 20, 2006
evolutionary computation, genetic network programming, data mining, association rules

A method of association rule mining using Genetic Network Programming (GNP) is proposed to improve the performance of association rule extraction. The proposed mechanisms can calculate measurements of association rules directly using GNP, and measure the significance of the association via the chi-squared test. Users can define the conditions of importance of association rules flexibly, which include the chi-squared value and the number of attributes in a rule. The proposed system evolves itself by an evolutionary method and obtains candidates of association rules by genetic operations. Extracted association rules are stored in a pool all together through generations and reflected in genetic operators as acquired information. Besides, our method can contain negation of attributes in association rules and suit association rule mining from dense databases. In this paper, we describe an extended algorithm capable of finding important association rules using GNP with sophisticated rule acquisition mechanisms and present some experimental results.

Cite this article as:
Kaoru Shimada, Kotaro Hirasawa, and Jinglu Hu, “Genetic Network Programming with Acquisition Mechanisms of Association Rules,” J. Adv. Comput. Intell. Intell. Inform., Vol.10, No.1, pp. 102-111, 2006.
Data files:
  1. [1] C. Zhang, and S. Zhang, “Association Rule Mining: models and algorithms,” Springer, 2002.
  2. [2] R. Agrawal, T. Imienski, and A. Swami, “Mining Association Rules between sets of items in massive databases,” In Proc. of the 1993 ACM SIGMOD Conf., pp. 207-216, 1993.
  3. [3] R. Agrawal, and R. Srikant, “Fast Algorithms for Mining Association Rules,” In Proc. of the 20th VLDB Conf., pp. 487-499, 1994.
  4. [4] S. Brin, R. Motwani, and C. Silverstein, “Beyond market baskets : generalizing association rules to correlations,” In Proc. of the 1997 ACM SIGMOD Conf., pp. 265-276, 1997.
  5. [5] J. S. Park, M. S. Chen, and P. S. Yu, “An Effective Hash-Based Algorithm for Mining Association Rules,” In Proc. of the 1995 ACM SIGMOD Conf., pp. 175-186, 1995.
  6. [6] R. J. Bayardo Jr., R. Agrawal, and D. Gunopulos, “Constraint-Based Rule Mining in Large, Dense Databases,” Proc. of the 15th International Conf. on Data Engineering, pp. 188-197, 1999.
  7. [7] A. K. H. Tung, H. Lu, J. Han, and L. Feng, “Efficient Mining of Intertransaction Association Rules,” IEEE Transactions on Knowledge and Data Engineering, Vol.15, No.1, pp. 43-56, 2003.
  8. [8] X. Wu, C. Zhang, and S. Zhang, “Efficient Mining of Both Positive and Negative Association Rules,” ACM Transactions on Information Systems, Vol.22, No.3, pp. 381-405, 2004.
  9. [9] K. Shimada, K. Hirasawa, and T. Furuzuki, “Association rule mining using genetic network programming,” The 10th International Symp. on Artificial Life and Robotics 2005, pp. 240-245, 2005.
  10. [10] M-L. Antonie, and O. R. Zaïane, “An Associative Classifier based on Positive and Negative Rules,” In Proc. of DMKD, pp. 64-69, 2004.
  11. [11] H. Katagiri, K. Hirasawa, and J. Hu, “Genetic network programming –application to intelligent agents–,” In Proc. of IEEE International Conf. on Syst., Man and Cybernetics, pp. 3829-3834, 2000.
  12. [12] H. Katagiri, K. Hirasawa, J. Hu, and J. Murata, “Network Structure Oriented Evolutionary Model – Genetic Network Programming,” In Proc. of Genetic and Evolutionary Computation Conference, pp. 219-226, 2001.
  13. [13] K. Hirasawa, M. Okubo, H. Katagiri, J. Hu, and J. Murata, “Comparison between Genetic Network Programming (GNP) and Genetic Programming (GP),” In Proc. of Congress of Evolutionary Computation, pp. 1276-1282, 2001.

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