Alternate Genetic Network Programming with Association Rules Acquisition Mechanisms Between Attribute Families
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
A method of association rule mining with chi-squared test using Alternate Genetic Network Programming (aGNP) is proposed. GNP is one of the evolutionary optimization techniques, which uses directed graph structures as genes. aGNP is an extended GNP in terms of including two kinds of node function sets. The proposed system can extract important association rules whose antecedent and consequent are composed of the attributes of each family defined by users. Rule extraction is done without identifying frequent itemsets used in Apriori-like methods. The method can be applied to rule extraction from dense database, and can extract dependent pairs of the sets of attributes in the database. Extracted rules are stored in a pool all together through generations and reflected in genetic operators as acquired information. In this paper, we describe the algorithm capable of finding the important association rules and present some experimental results.
-  C. Zhang and S. Zhang, “Association Rule Mining: models and algorithms,” Springer, 2002.
-  R. Agrawal and R. Srikant, “Fast Algorithms for Mining Association Rules,” In Proc. of the 20th VLDB Conf., pp. 487-499, 1994.
-  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.
-  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.
-  T. Eguchi, K. Hirasawa, J. Hu, and N. Ota, “A Study of Evolutionary Multiagent Models Based on Symbiosis,” IEEE Trans. on System, Man and Cybernetics –PART B–, Vol.35, No.1, pp. 179-193, 2006.
-  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.
-  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.
-  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.
-  R. J. Bayardo Jr., R. Agrawal, and D. Gunopulos, “Constraint-Based Rule Mining in Large, Dense Databases,” In Proc. of the 15th International Conf. on Data Engineering, pp. 188-197, 1999.
-  S. Brin, R. Motwani, and C. Silverstein, “Beyond market baskets: generalizing association rules to correlations,” In Proc. of ACM SIGMOD, pp. 265-276, 1997.
-  S. Eto, H. Hatakeyama, S. Mabu, K. Hirasawa, and J. Hu, “Realizing Functional Localization Using Genetic Network Programming with Importance Index,” Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol.10, No.4, pp. 555-566, 2006.
-  C. Blake and C. Merz, UCI repository of machine learning databases.
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