Compression-Based Measures for Mining Interesting Rules

Author:

Suzuki Einoshin

Publisher

Springer Berlin Heidelberg

Reference31 articles.

1. Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., Verkamo, A.I., et al.: Fast discovery of association rules. In: Fayyad, U.M., et al. (eds.) Advances in Knowledge Discovery and Data Mining, pp. 307–328. AAAI/MIT Press, Menlo Park (1996)

2. Lecture Notes in Computer Science;J.. P. Barthélemy,2006

3. Bayardo, R.J., Agrawal, R.: Mining the Most Interesting Rules. In: Proc. Fifth ACM SIGKDD Int’l Conf. on Knowledge Discovery and Data Mining, pp. 145–154 (1999)

4. Brin, S., Motwani, R., Silverstein, C.: Beyond Market Baskets: Generalizing Association Rules to Correlations. In: SIGMOD 1997, Proc. ACM SIGMOD Int’l Conf. on Management of Data, pp. 265–276 (1997)

5. Lecture Notes in Computer Science;D.R. Carvalho,2005

Cited by 2 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. Mining Undominated Association Rules Through Interestingness Measures;International Journal on Artificial Intelligence Tools;2014-08

2. Common Sense Knowledge Based Hybrid Interestingness Measures for Data Mining;Convergence and Hybrid Information Technology;2012

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