Deducing Bounds on the Support of Itemsets-Reference-Cited by-同舟云学术

Deducing Bounds on the Support of Itemsets

Published:2004 Issue: Volume: Page:214-233
ISSN:0302-9743
Container-title:Database Support for Data Mining Applications
language:
Short-container-title:

Author:

Calders Toon

Publisher

Springer Berlin Heidelberg

Link

http://link.springer.com/content/pdf/10.1007/978-3-540-44497-8_11

Reference20 articles.

1. Agrawal, R., et al.: Mining association rules between sets of items in large databases. In: Proc. ACM SIGMOD, pp. 207–216 (1993)

2. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proc. VLDB, pp. 487–499 (1994)

3. Blake, C.L., Merz, C.J.: UCI Repository of machine learning databases. University of California, Dept. of Inf. and CS., Irvine, CA (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html

4. Lecture Notes in Computer Science;J.-F. Boulicaut,2000

5. Lecture Notes in Artificial Intelligence;J.-F. Boulicaut,2000

Cited by 5 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. Estimating Prevalence Bounds of Temporal Association Patterns to Discover Temporally Similar Patterns;Recent Advances in Soft Computing;2017

2. A Computationally Efficient Approach for Mining Similar Temporal Patterns;Recent Advances in Soft Computing;2017

3. Non-derivable itemset mining;Data Mining and Knowledge Discovery;2007-01-26

4. A Survey on Condensed Representations for Frequent Sets;Constraint-Based Mining and Inductive Databases;2006

5. Transaction Databases, Frequent Itemsets, and Their Condensed Representations;Lecture Notes in Computer Science;2006