Efficient mining of weighted association rules (WAR)

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Efficient mining of weighted association rules (WAR)

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International Conference on Knowledge Discovery and Data Mining archive
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining table of contents

Boston, Massachusetts, United States

Pages: 270 - 274

Year of Publication: 2000

ISBN:1-58113-233-6

Authors

Wei Wang	IBM Watson Research Center
Jiong Yang	IBM Watson Research Center
Philip S. Yu	IBM Watson Research Center

Sponsors

SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
AAAI : Am Assoc for Artifical Intelligence
SIGART: ACM Special Interest Group on Artificial Intelligence
SIGMOD: ACM Special Interest Group on Management of Data

Publisher

ACM New York, NY, USA

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Downloads (6 Weeks): 10, Downloads (12 Months): 72, Citation Count: 6

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REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Rakesh Agrawal , Ramakrishnan Srikant, Fast Algorithms for Mining Association Rules in Large Databases, Proceedings of the 20th International Conference on Very Large Data Bases, p.487-499, September 12-15, 1994
	2	Rakesh Agrawal , Johannes Gehrke , Dimitrios Gunopulos , Prabhakar Raghavan, Automatic subspace clustering of high dimensional data for data mining applications, Proceedings of the 1998 ACM SIGMOD international conference on Management of data, p.94-105, June 01-04, 1998, Seattle, Washington, United States
	3	Ramakrishnan Srikant , Rakesh Agrawal, Mining quantitative association rules in large relational tables, Proceedings of the 1996 ACM SIGMOD international conference on Management of data, p.1-12, June 04-06, 1996, Montreal, Quebec, Canada
	4	W. Wang, J. Yang, and P. Yu. Efficient mining of weighted association rules (WAR). IBM Research Report RC 21692(97734), March, 2000.

CITED BY 6

	Feng Tao , Fionn Murtagh , Mohsen Farid, Weighted Association Rule Mining using weighted support and significance framework, Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, August 24-27, 2003, Washington, D.C.
	Ying Liu , Wei-keng Liao , Alok Choudhary, A fast high utility itemsets mining algorithm, Proceedings of the 1st international workshop on Utility-based data mining, p.90-99, August 21-21, 2005, Chicago, Illinois
	Amihood Amir , Yonatan Aumann , Ronen Feldman , Moshe Fresko, Maximal Association Rules: A Tool for Mining Associations in Text, Journal of Intelligent Information Systems, v.25 n.3, p.333-345, November 2005
	Unil Yun, A new framework for detecting weighted sequential patterns in large sequence databases, Knowledge-Based Systems, v.21 n.2, p.110-122, March, 2008
	Jianying Hu , Aleksandra Mojsilovic, High-utility pattern mining: A method for discovery of high-utility item sets, Pattern Recognition, v.40 n.11, p.3317-3324, November, 2007
	Runian Geng , Wenbo Xu , Xiangjun Dong, Efficient mining of interesting weighted patterns from directed graph traversals, Integrated Computer-Aided Engineering, v.16 n.1, p.21-49, January 2009