Abstract
Apriori-like algorithms for association rules mining rely upon the minimum support and the minimum confidence. Users often feel hard to give these thresholds. On the other hand, genetic algorithm is effective for global searching, especially when the searching space is so large that it is hardly possible to use deterministic searching method. We try to apply genetic algorithm to the association rules mining and propose an evolutionary method. Computations are conducted, showing that our ARMGA model can be used for the automation of the association rule mining systems, and the ideas given in this paper are effective.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, Washington, DC, pp. 207–216 (May 1993)
Wu, X., Zhang, C., Zhang, S.: Mining both positive and negative association rules. In: Proceedings of 19th International Conference on Machine Learning, Sydney, Australia, July 2002, pp. 658–665 (2002)
Freitas, A.: A survey of evolutionary algorithms for data mining and knowledge discovery. In: Advances in Evolutionary Computation. Springer, Berlin (2002)
Fidelis, M., Lopes, H., Freitas, A.: Discovering comprehensible classification rules with a genetic algorithm. In: Proc. of the 2000 Congress on Evolutionary Computation, La Jolla, CA, USA, July 2000, pp. 805–810 (2000)
Pei, M., Goodman, E., Punch, W.: Pattern discovery from data using genetic algorithm. In: Proc. 1st Pacific- Asia Conf. Knowledge Discovery and Data Mining (Febraury 1997)
Augier, S., Venturini, G., Kodratoff, Y.: Learning first order logic rules with a genetic algorithm. In: Proc. 1st Int. Conf. Knowledge Discovery and Data Mining, Montreal, Canada, pp. 21–26 (1995)
Au, W., Chan, C.: An evolutionary approach for discovering changing patterns in historical data. In: Proc. of SPIE, vol. 4730, pp. 398–409 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yan, X., Zhang, C., Zhang, S. (2003). A Database-Independent Approach of Mining Association Rules with Genetic Algorithm. In: Liu, J., Cheung, Ym., Yin, H. (eds) Intelligent Data Engineering and Automated Learning. IDEAL 2003. Lecture Notes in Computer Science, vol 2690. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45080-1_123
Download citation
DOI: https://doi.org/10.1007/978-3-540-45080-1_123
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40550-4
Online ISBN: 978-3-540-45080-1
eBook Packages: Springer Book Archive