The Hows, Whys, and Whens of Constraints in Itemset and Rule Discovery-Reference-Cited by-同舟云学术

The Hows, Whys, and Whens of Constraints in Itemset and Rule Discovery

Published:2006 Issue: Volume: Page:1-13
ISSN:0302-9743
Container-title:Constraint-Based Mining and Inductive Databases
language:
Short-container-title:

Author:

Bayardo Roberto J.

Publisher

Springer Berlin Heidelberg

Link

http://link.springer.com/content/pdf/10.1007/11615576_1

Reference25 articles.

1. Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rules between Sets of Items in Large Databases. In: Proc. of the 1993 ACM-SIGMOD Conf. on Management of Data, pp. 207–216 (1993)

2. Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., Verkamo, A.I.: Fast Discovery of Association Rules. In: Advances in Knowledge Discovery and Data Mining, pp. 307–328. AAAI Press, Menlo Park (1996)

3. Antunes, C., Oliveira, A.L.: Mining Patterns Using Relaxations of User Defined Constraints. In: Proc. of the Workshop on Knowledge Discovery in Inductive Databases (2004)

4. Bayardo, R.J.: Efficiently Mining Long Patterns from Databases. In: Proc. of the 1998 ACM-SIGMOD Int’l Conf. on Management of Data, pp. 85–93 (1998)

5. Bayardo, R.J.: The many roles of constraints in data mining (Letter from the guest editor.). ACM SIGKDD Explorations 4(1), i–ii (2002)

Cited by 9 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. Knowledge redundancy approach to reduce size in association rules;Informatica;2020-06-15

2. Pushing Constraints into a Pattern-Tree;Modeling Decisions for Artificial Intelligence;2013

3. Nonredundant Generalized Rules and Their Impact in Classification;Advances in Intelligent Information Systems;2010

4. Discovering Knowledge from Local Patterns in SAGE Data;Data Mining and Medical Knowledge Management;2009

5. Feature Construction Based on Closedness Properties Is Not That Simple;Advances in Knowledge Discovery and Data Mining;2008