BruteSuppression: a size reduction method for Apriori rule sets-Reference-Cited by-同舟云学术

BruteSuppression: a size reduction method for Apriori rule sets

Published:2013-01-10 Issue:3 Volume:40 Page:431-454
ISSN:0925-9902
Container-title:Journal of Intelligent Information Systems
language:en
Short-container-title:J Intell Inf Syst

Author:

Hills Jon,Bagnall Anthony,de la Iglesia Beatriz,Richards Graeme

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,Hardware and Architecture,Information Systems,Software

Link

http://link.springer.com/content/pdf/10.1007/s10844-012-0232-5.pdf

Reference27 articles.

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2. Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules. In Proc. 20th int. conf. very large data bases, VLDB (Vol. 1215, pp. 487–499).

3. Ali, K., Manganaris, S., Srikant, R. (1997). Partial classification using association rules. In Proceedings of the third international conference on knowledge discovery and data mining (pp. 115–118).

4. Balcázar, J. (2009). Confidence width: An objective measure for association rule novelty. In Workshop on quality issues, measures of interestingness and evaluation of data mining models QIMIE (Vol. 9).

5. Bayardo, R., Agrawal, R., Gunopulos, D. (2000). Constraint-based rule mining in large, dense databases. Data Mining and Knowledge Discovery, 4(2), 217–240.

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