Quantitative Redundancy in Partial Implications

Author:

Balcázar José L.

Publisher

Springer International Publishing

Reference44 articles.

1. Aggarwal, C.C., Han, J. (eds.): Frequent Pattern Mining. Springer, Switzerland (2014)

2. Aggarwal, C.C., Yu, P.S.: A new approach to online generation of association rules. IEEE Trans. Knowl. Data Eng. 13(4), 527–540 (2001)

3. Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: Buneman, P., Jajodia, S. (eds.) SIGMOD Conference, pp. 207–216. ACM Press (1993)

4. Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., Verkamo, A.I.: Fast discovery of association rules. In: Piatetsky-Shapiro, G., Frawley, W. (eds.) Advances in Knowledge Discovery and Data Mining, pp. 307–328. AAAI/MIT Press, Menlo Park (1996)

5. Atserias, A., Balcázar, J.L.: Entailment among probabilistic implications. Accepted for presentation at LICS (2015)

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