Efficient search for association rules

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Efficient search for association rules

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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: 99 - 107

Year of Publication: 2000

ISBN:1-58113-233-6

Author

Geoffrey I. Webb

School of Computing and Mathematics, Deakin University, Geelong, Vic. 3217, Australia

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

Bibliometrics

Downloads (6 Weeks): 8, Downloads (12 Months): 70, Citation Count: 20

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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.

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	2	Rakesh Agrawal , Heikki Mannila , Ramakrishnan Srikant , Hannu Toivonen , A. Inkeri Verkamo, Fast discovery of association rules, Advances in knowledge discovery and data mining, American Association for Artificial Intelligence, Menlo Park, CA, 1996
	3	Roberto J. Bayardo, Jr. , Rakesh Agrawal, Mining the most interesting rules, Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, p.145-154, August 15-18, 1999, San Diego, California, United States [doi>10.1145/312129.312219]
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	Zijian Zheng , Ron Kohavi , Llew Mason, Real world performance of association rule algorithms, Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, p.401-406, August 26-29, 2001, San Francisco, California
	Xindong Wu , Shichao Zhang, Synthesizing High-Frequency Rules from Different Data Sources, IEEE Transactions on Knowledge and Data Engineering, v.15 n.2, p.353-367, February 2003
	Ron Kohavi , Llew Mason , Rajesh Parekh , Zijian Zheng, Lessons and Challenges from Mining Retail E-Commerce Data, Machine Learning, v.57 n.1-2, p.83-113, October-November 2004
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	Shichao Zhang , Li Liu, Mining dynamic databases by weighting, Acta Cybernetica, v.16 n.1, p.179-205, January 2003
	Shichao Zhang , Chengqi Zhang , Jeffrey Xu Yu, An efficient strategy for mining exceptions in multi-databases, Information Sciences: an International Journal, v.165 n.1-2, p.1-20, 3 September 2004
	Jiuyong Li , Hong Shen , Rodney Topor, Mining Informative Rule Set for Prediction, Journal of Intelligent Information Systems, v.22 n.2, p.155-174, March 2004
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	B. Raphael , B. Domer , S. Saitta , I. F. C. Smith, Incremental development of CBR strategies for computing project cost probabilities, Advanced Engineering Informatics, v.21 n.3, p.311-321, July, 2007
	Weiyang Lin , Sergio A. Alvarez , Carolina Ruiz, Efficient Adaptive-Support Association Rule Mining for Recommender Systems, Data Mining and Knowledge Discovery, v.6 n.1, p.83-105, January 2002
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	Jeffrey Xu Yu , Zhiheng Li , Guimei Liu, A data mining proxy approach for efficient frequent itemset mining, The VLDB Journal — The International Journal on Very Large Data Bases, v.17 n.4, p.947-970, July 2008
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	Junfeng Ding , Stephen S. T. Yau, TCOM, an innovative data structure for mining association rules among infrequent items, Computers & Mathematics with Applications, v.57 n.2, p.290-301, January, 2009
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