关联分类算法的研究

关联分类算法的研究内容摘要：

idate Generation. In Proc. of the ACMSIGMOD 2020 Int. Conf. on Management of Data (SIGMOD’00), −12, Dallas, May 2020. [3] W. Li, J. Han and J. Pei. CMAR: Accurate and Efficient Classification Based on Multiple ClassAssociation Rules. In Proc. of 2020 IEEE Int. Conf. on Data Mining (ICDM39。 01), , San Jose CA, Nov 2020. [4] J. Li, G. Dong, K. Ramamohanarao and L. Wong. DeEPs: A New InstanceBased Lazy Discovery and Classification System. Machine Learning. 54, , 2020. [5] Adriano Veloso, Wagner Meira Jr, and Mohammed J. Zaki. Lazy Association Classification. In Proc. of 2020 IEEE Int. Conf. on Data Mining (ICDM39。 06), , Hong Kong, Oct 2020. [6] MariaLuiza Antonie, Osmar R. Zaiane, and Robert C. Holte. Learning to Use a Learned Model: A TwoStage Approach to Classification. In Proc. of 2020 IEEE Int. Conf. on Data Mining (ICDM39。 06), , Hong Kong, Oct 2020. [7] Abdelaziz Berrado, Gee C. Runger. Using Metarules to Organize and Group Discovered Association Rules. Data Mining and Knowledge Discover. 14: 409431, 2020. [8] F. Thabtah, P. Cowling, and Y. Peng. MCAR: Multiclass Classification based on Association Rule Approach. In Proceeding of the 3rd IEEE International Conference on Computer Systems and Applications. . Cairo, Egypt. Hebei University [9] O. R. Zaiane and . Antonie. On pruning and tuning rules for associative classifiers. In Proc. of Int39。 l Conf. on KnowledgeBased Intelligence Information amp。 Engineering Systems (KES39。 05), , 2020. [。