JACIII Vol.12 No.4 pp. 393-403
doi: 10.20965/jaciii.2008.p0393


Comparative Association Rules Mining Using Genetic Network Programming (GNP) with Attributes Accumulation Mechanism and its Application to Traffic Systems

Wei Wei, Huiyu Zhou, Kaoru Shimada, Shingo Mabu,
and Kotaro Hirasawa

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

December 14, 2007
May 2, 2008
July 20, 2008
evolutionary computation, genetic network programming (GNP), comparative association mining, traffic system
Among several methods of extracting association rules that have been reported, a new evolutionary method named Genetic Network Programming (GNP) has also shown its effectiveness for dense databases. However, the conventional GNP data mining method can not find comparative relations and hidden patterns among a large amount of data. In this paper, we present a method of comparative association rules mining using Genetic Network Programming (GNP) with attributes accumulation mechanism in order to uncover comparative association rules between different datasets. GNP is an evolutionary approach recently developed, which can evolve itself and find the optimal solutions. The objective of the comparative association rules mining is to check two or more databases instead of one, so as to find the hidden relations among them. The proposed method measures the importance of association rules by using the absolute values of the confidence differences of the rules obtained from different databases and can get a number of interesting rules. Association rules obtained by comparison can help us to find and analyze the explicit and implicit patterns among a large amount of data. On the other hand, the calculation is very time-consuming, when the conventional GNP based data mining is used for the large attributes case. So, we have proposed an attributes accumulation mechanism to improve the performances. Then, the comparative association rules mining using GNP has been applied to a complicated traffic system. By mining and analyzing the rules under different traffic situations, it was found that we can get interesting information of the traffic system.
Cite this article as:
W. Wei, H. Zhou, K. Shimada, S. Mabu, and K. Hirasawa, “Comparative Association Rules Mining Using Genetic Network Programming (GNP) with Attributes Accumulation Mechanism and its Application to Traffic Systems,” J. Adv. Comput. Intell. Intell. Inform., Vol.12 No.4, pp. 393-403, 2008.
Data files:
  1. [1] C. Zhang and S. Zhang, “Association Rule Mining: models and algorithms,” Springer, 2002.
  2. [2] 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.
  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] M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonen, and A. I. Verkamo, “Finding Interesting Rules from Large Sets of Discovered Association rules,” Proc. Third Int. Conf. Information and Knowledge Management, pp. 401-408, Nov. 1994.
  5. [5] A. Savasere, E. Omiecinski, and S. Navathe, “An Efficient Algorithm for Mining Association Rules in Large Databases,” Proc. 1995 Int. Conf. Very Large Data Bases, pp. 432-443, Sept. 1995.
  6. [6] J. H. Holland, “Adaptation in Natural and Artificial Systems,” Ann Arbor: University of Michigan Press, 1975.
  7. [7] D. E. Goldberg, “Genetic Algorithm in search, optimization and machine learning,” Addison-Wesley, 1989.
  8. [8] J. R. Koza, “Genetic Programming, on the programming of computers by means of natural selection,” Cambridge, Mass., MIT Press, 1992.
  9. [9] J. R. Koza, “Genetic Programming II, Automatic Discovery of Reusable Programs,” Cambridge, Mass., MIT Press, 1994.
  10. [10] S. Mabu, K. Hirasawa, and J. Hu, “A Graph-Based Evolutionary Algorithm: Genetic Network Programming (GNP) and Its Extension Using Reinforcement Learning,” Evolutionary Computation, MIT Press, Vol.15, No.3, pp. 369-398, 2007.
  11. [11] K. Shimada, K. Hirasawa, and J. Hu, “Genetic Network Programming with Acquisition Mechanisms of Association Rules,” Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol.10, No.1, pp. 102-111, 2006.
  12. [12] T. Eguchi, K. Hirasawa, J. Hu, and N. Ota, “Study of evolutionary multiagent models based on symbiosis,” IEEE Trans. Syst., Man and Cybern. B, Vol.36, No.1, pp. 179-193, 2006.
  13. [13] X. Wu, C. Zhang, and S. Zhang, “Efficient Mining of Both Positive and Negative Association Rule,” ACM Transactions on Information Systems, Vol.22, No.3, pp. 381-405, 2004.
  14. [14] A. K. H. Tung, H. Lu, J. Han, and L. Feng, “Efficient Mining of Intertransaction Association Rule,” IEEE Transactions on Knowledge and Data Engineering, Vol.15, No.1, pp. 43-56, 2003.
  15. [15] R. J. Bayardo Jr., R. Agrawal, and D. Gunopulos, “Constraint-Based Rule Mining in Large, Dense Databases,” In Proc. of the 15th Int. Conf. on Data Engineering, pp. 188-197, 1999.
  16. [16] 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.
  17. [17] K. Shimada, K. Hirasawa, and T. Furuzuki, “Association rule mining using genetic network programming,” The 10th Int. Symp. on Artificial Life and Robotics 2005, pp. 240-245, 2005.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Jun. 03, 2024