Detecting change in categorical data

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Detecting change in categorical data: mining contrast sets

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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: 302 - 306

Year of Publication: 1999

ISBN:1-58113-143-7

Authors

Stephen D. Bay	Department of Information and Computer Science, University of California, Irvine, Irvine, CA
Michael J. Pazzani	Department of Information and Computer Science, University of California, Irvine, Irvine, CA

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): 6, Downloads (12 Months): 53, Citation Count: 18

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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
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	8	P. Hoschka and W. K!Ssgen. A support system for interpreting statistical data. In G. Piatetsky-Shapiro and W. J. Frawley, editors, Knowledge Discovery in Databases, pages 325-346. AAAI Press, 1991.
	9	P. Riddle, R. Segal, and O. Etzioni. Representation design and brute-force induction in a Boeing manufacturing domain. Applied Artificial Intelligence, 8:125-147, 1994.
	10	J. P. Shaffer. Multiple hypothesis testing. Annual Review Psychology, 46:561-584, 1995.

CITED BY 18

	Stephen D. Bay, Multivariate discretization of continuous variables for set mining, Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, p.315-319, August 20-23, 2000, Boston, Massachusetts, United States
	Tim Menzies , Ying Hu, Data Mining for Very Busy People, Computer, v.36 n.11, p.22-29, November 2003
	Liqiang Geng , Howard J. Hamilton, Interestingness measures for data mining: A survey, ACM Computing Surveys (CSUR), v.38 n.3, p.9-es, 2006
	Hong Zhang , Balaji Padmanabhan , Alexander Tuzhilin, On the discovery of significant statistical quantitative rules, Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, August 22-25, 2004, Seattle, WA, USA
	Weng-Keen Wong , Andrew Moore , Gregory Cooper , Michael Wagner, Rule-based anomaly pattern detection for detecting disease outbreaks, Eighteenth national conference on Artificial intelligence, p.217-223, July 28-August 01, 2002, Edmonton, Alberta, Canada
	Sasha Ivkovic , John Yearwood , Andrew Stranieri, Visualizing association rules for feedback within the legal system, Proceedings of the 9th international conference on Artificial intelligence and law, June 24-28, 2003, Scotland, United Kingdom
	Jianyong Wang , Jiawei Han , Ying Lu , Petre Tzvetkov, TFP: An Efficient Algorithm for Mining Top-K Frequent Closed Itemsets, IEEE Transactions on Knowledge and Data Engineering, v.17 n.5, p.652-664, May 2005
	Tim Menzies , Ying Hu, Just enough learning (of association rules): the TAR2 "Treatment" learner, Artificial Intelligence Review, v.25 n.3, p.211-229, May 2006
	Siegfried Nijssen , Tias Guns , Luc De Raedt, Correlated itemset mining in ROC space: a constraint programming approach, Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, June 28-July 01, 2009, Paris, France
	Yu Li , Miroslav Kubat, Searching for high-support itemsets in itemset trees, Intelligent Data Analysis, v.10 n.2, p.105-120, March 2006
	Weng-Keen Wong , Andrew Moore , Gregory Cooper , Michael Wagner, What's Strange About Recent Events (WSARE): An Algorithm for the Early Detection of Disease Outbreaks, The Journal of Machine Learning Research, 6, p.1961-1998, 12/1/2005
	Fabrício Enembreck , Jean-Paul Brathès, ELA—A new Approach for Learning Agents, Autonomous Agents and Multi-Agent Systems, v.10 n.3, p.215-248, May 2005
	Yongsong Qin , Shichao Zhang, Empirical likelihood confidence intervals for differences between two datasets with missing data, Pattern Recognition Letters, v.29 n.6, p.803-812, April, 2008
	Stephen D. Bay , Michael J. Pazzani, Detecting Group Differences: Mining Contrast Sets, Data Mining and Knowledge Discovery, v.5 n.3, p.213-246, July 2001
	Yongsong Qin , Shichao Zhang , Xiaofeng Zhu , Jilian Zhang , Chengqi Zhang, Estimating confidence intervals for structural differences between contrast groups with missing data, Expert Systems with Applications: An International Journal, v.36 n.3, p.6431-6438, April, 2009
	Omid Jalali , Tim Menzies , Martin Feather, Optimizing requirements decisions with keys, Proceedings of the 4th international workshop on Predictor models in software engineering, May 12-13, 2008, Leipzig, Germany
	Ying Yang , Xindong Wu , Xingquan Zhu, Conceptual equivalence for contrast mining in classification learning, Data & Knowledge Engineering, v.67 n.3, p.413-429, December, 2008
	Jilian Zhang , Shichao Zhang , Xiaofeng Zhu , Xindong Wu , Chengqi Zhang, Measuring the uncertainty of differences for contrasting groups, Proceedings of the 22nd national conference on Artificial intelligence, p.1920-1921, July 22-26, 2007, Vancouver, British Columbia, Canada