A statistical theory for quantitative association rules

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A statistical theory for quantitative association rules

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

San Diego, California, United States

Pages: 261 - 270

Year of Publication: 1999

ISBN:1-58113-143-7

Authors

Yonatan Aumann	Bar-Ilan University, Department of Computer Science, Ramat, Gan, Israel 52900
Yehuda Lindell	The Weizmann Institute of Science, Faculty of Mathematics and Computer Science, Rehovot 76100, Israel

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): 5, Downloads (12 Months): 39, Citation Count: 23

Additional Information:

references cited by index terms collaborative colleagues

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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 , 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
	3	Sergey Brin , Rajeev Motwani , Craig Silverstein, Beyond market baskets: generalizing association rules to correlations, Proceedings of the 1997 ACM SIGMOD international conference on Management of data, p.265-276, May 11-15, 1997, Tucson, Arizona, United States
	4	Takeshi Fukuda , Yasukiko Morimoto , Shinichi Morishita , Takeshi Tokuyama, Data mining using two-dimensional optimized association rules: scheme, algorithms, and visualization, Proceedings of the 1996 ACM SIGMOD international conference on Management of data, p.13-23, June 04-06, 1996, Montreal, Quebec, Canada
	5	Lindgren, Bernard W. Statistical Theory. Macmillan Publishing Co., Inc. New York, 1976.
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	7	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
	8	Hannu Toivonen, Sampling Large Databases for Association Rules, Proceedings of the 22th International Conference on Very Large Data Bases, p.134-145, September 03-06, 1996
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	10	Z. Zhing, Y. Lu and B. Zhang. An Effective Partitioning-Combining Algorithm for Discovering Quantitative Association Rules. Proc. of the First Pacific-Asia Conference on Knowledge Discovery and Data Mining, 1997.

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	Fernando Berzal , Juan-Carlos Cubero , Daniel Sánchez , José María Serrano, ART: A Hybrid Classification Model, Machine Learning, v.54 n.1, p.67-92, January 2004
	Guozhu Dong , Jiawei Han , Joyce M. W. Lam , Jian Pei , Ke Wang , Wei Zou, Mining Constrained Gradients in Large Databases, IEEE Transactions on Knowledge and Data Engineering, v.16 n.8, p.922-938, August 2004
	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
	J. Mata , J. L. Alvarez , J. C. Riquelme, An evolutionary algorithm to discover numeric association rules, Proceedings of the 2002 ACM symposium on Applied computing, March 11-14, 2002, Madrid, Spain
	Toon Calders , Bart Goethals , Szymon Jaroszewicz, Mining rank-correlated sets of numerical attributes, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, August 20-23, 2006, Philadelphia, PA, USA
	B. Pôssas , M. Carvalho , R. Resende , W. Meita, Jr., Using quantitative information for efficient association rule generation, ACM SIGMOD Record, v.29 n.4, p.19-25, Dec. 2000
	Joaquim C. Felipe , Marcela X. Ribeiro , Elaine P. M. Sousa , Agma J. M. Traina , Caetano Jr Traina, Effective shape-based retrieval and classification of mammograms, Proceedings of the 2006 ACM symposium on Applied computing, April 23-27, 2006, Dijon, France
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