Efficient Pruning of Class Association Rules Using Statistics and Genetic Relation Algorithm-Reference-Cited by-同舟云学术

Efficient Pruning of Class Association Rules Using Statistics and Genetic Relation Algorithm

Published:2010-09-01 Issue:5 Volume:3 Page:336-345
ISSN:1882-4889
Container-title:SICE Journal of Control, Measurement, and System Integration
language:en
Short-container-title:SICE Journal of Control, Measurement, and System Integration

Author:

Gonzales Eloy¹,Mabu Shingo¹,Taboada Karla¹,Shimada Kaoru²,Hirasawa Kotaro¹

Affiliation:

1. Graduate School of Information, Production and Systems, Waseda University

2. Information, Production and Systems Research Center, Waseda University

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.9746/jcmsi.3.336

Reference18 articles.

1. [1] A.A. Freitas: Data Mining and Knowledge Discovery with Evolutionary Algorithms, Springer-Verlag, 2002.

2. [2] K. Shimada, K. Hirasawa, and J. Hu: Class association rule mining with chi-squared test using genetic network programming, Proc. IEEE International Conference on Systems, Man and Cybernetics, pp. 5338-5344, 2006.

3. [3] K. Shimada, K. Hirasawa, and T. Furuzuki: 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.

4. [4] N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal: Discovering frequent closed itemsets for association rules, Proc. 7th International Conference on Database Theory, LNCS 1540, pp. 398-416, 1999.

5. [5] M.J. Zaki: Generating non-redundant association rules, Proc. 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 34-43, 2000.

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