Publisher
Springer Berlin Heidelberg
Reference13 articles.
1. R. Agrawal, T. Imielinski, and A. Swami. Data mining: a performance perspective. IEEE Transactions on Knowledge and Data Engineering, 5(6): 914–925, 1993.
2. R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. In Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, pages 207–216, Washington, D. C., 1993.
3. M.-S. Chen, J. Han, and P. S. Yu. Data mining: an overview from a database perspective. IEEE Transactions on Knowledge and Data Engineering, 8(6): 866–882, 1996.
4. Y. Fu and J. Han, Meta-rule-guided mining of association rules in relational databases. Proc. 1995 Int'l Workshop. on Knowledge Discovery and Deductive and Object-Oriented Databases (KDOOD'95), Singapore, December 1995, pp. 39–46.
5. M. Klemettinen, H. Mannila, P Ronkainen, H. Toivonen, and A. I. Verkamo. Finding interesting rules from large sets of discovered association rules. In Proceedings of the Third International Conference on Information and Knowledge Management, pages 401–407, Gaithersburg, Maryland, 1994.
Cited by
29 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Peculiarity and Diversity Measures to Evaluate Attribute-Based Access Rules;2023 5th IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA);2023-11-01
2. Clustering association rules to build beliefs and discover unexpected patterns;Applied Intelligence;2020-02-20
3. MARAS;Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining;2017-08-13
4. Mining the change of customer behavior in dynamic markets;Information Technology and Management;2014-09-05
5. Interestingness Measures for Multi-Level Association Rules;Studies in Computational Intelligence;2013-11-15