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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: 6 - 15

Year of Publication: 1999

ISBN:1-58113-143-7

Authors

William DuMouchel	AT&T Labs-Research
Chris Volinsky	AT&T Labs-Research
Theodore Johnson	AT&T Labs-Research
Corinna Cortes	AT&T Labs-Research
Daryl Pregibon	AT&T Labs-Research

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): 1, Downloads (12 Months): 49, Citation Count: 19

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.

	1	Barbara, D. (1997).The New Jersey data reduction report. Bulletin on the Technical Committee on Data Engineering 20(4), 3-45.
	2	Bradley, P. S., U. Fayyad, and C. Reina (1998). Scaling clustering algorithms to large databases. In Proc. d th Intl. Conf. on Knowledge Discovery and Data Mining (KDD), pp. 9-15.
	3	DuMouchel, W. (1999). Bayesian data mining in large frequency tables, with an application to the FDA spontaneous reporting system, with discussion. The American Statistician, in press.
	4	Johnson, T. and T. Dasu (1998). Comparing massive high dimensional data sets. in Proc. dth Intl. Conf. on Knowledge Discovery and Data Mining (KDD), pp. 229-233.
	5	Tian Zhang , Raghu Ramakrishnan , Miron Livny, BIRCH: A New Data Clustering Algorithm and Its Applications, Data Mining and Knowledge Discovery, v.1 n.2, p.141-182, 1997 [doi>10.1023/A:1009783824328]

CITED BY 19

	William DuMouchel, Data squashing: constructing summary data sets, Handbook of massive data sets, Kluwer Academic Publishers, Norwell, MA, 2002
	William DuMouchel , Daryl Pregibon, Empirical bayes screening for multi-item associations, Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, p.67-76, August 26-29, 2001, San Francisco, California
	Wai Lam , Chi-Kin Keung , Danyu Liu, Discovering Useful Concept Prototypes for Classification Based on Filtering and Abstraction, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.24 n.8, p.1075-1090, August 2002
	Markus M. Breunig , Hans-Peter Kriegel , Peer Kröger , Jörg Sander, Data bubbles: quality preserving performance boosting for hierarchical clustering, ACM SIGMOD Record, v.30 n.2, p.79-90, June 2001
	Foster Provost , Venkateswarlu Kolluri, Data mining tasks and methods: scalability, Handbook of data mining and knowledge discovery, Oxford University Press, Inc., New York, NY, 2002
	Carlos Ordonez , Paul Cereghini, SQLEM: fast clustering in SQL using the EM algorithm, ACM SIGMOD Record, v.29 n.2, p.559-570, June 2000
	Josep Domingo-Ferrer , Vicenç Torra, On the connections between statistical disclosure control for microdata and some artificial intelligence tools, Information Sciences—Informatics and Computer Science: An International Journal, 151, p.153-170, May 2003
	David M. Fram , June S. Almenoff , William DuMouchel, Empirical Bayesian data mining for discovering patterns in post-marketing drug safety, Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, August 24-27, 2003, Washington, D.C.
	William DuMouchel , Deepak K. Agarwal, Applications of sampling and fractional factorial designs to model-free data squashing, Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, August 24-27, 2003, Washington, D.C.
	Greg Ridgeway, Looking for lumps: boosting and bagging for density estimation, Computational Statistics & Data Analysis, v.38 n.4, p.379-392, 28 February 2002
	Tae-Wan Ryu , Christoph F. Eick, A database clustering methodology and tool, Information Sciences—Informatics and Computer Science: An International Journal, v.171 n.1-3, p.29-59, 4 March 2005
	Tina Eliassi-Rad , Terence Critchlow, A hybrid approach for multiresolution modeling of large-scale scientific data, Proceedings of the 2005 ACM symposium on Applied computing, March 13-17, 2005, Santa Fe, New Mexico
	Jianjun Zhou , Jörg Sander, Data bubbles for non-vector data: speeding-up hierarchical clustering in arbitrary metric spaces, Proceedings of the 29th international conference on Very large data bases, p.452-463, September 09-12, 2003, Berlin, Germany
	Art Owen, Data Squashing by Empirical Likelihood, Data Mining and Knowledge Discovery, v.7 n.1, p.101-113, January 2003
	Huan Liu , Hiroshi Motoda, On Issues of Instance Selection, Data Mining and Knowledge Discovery, v.6 n.2, p.115-130, April 2002
	David Madigan , Nandini Raghavan , William Dumouchel , Martha Nason , Christian Posse , Greg Ridgeway, Likelihood-Based Data Squashing: A Modeling Approach to Instance Construction, Data Mining and Knowledge Discovery, v.6 n.2, p.173-190, April 2002
	Thomas Reinartz, A Unifying View on Instance Selection, Data Mining and Knowledge Discovery, v.6 n.2, p.191-210, April 2002
	Le Song , Xinhua Zhang , Alex Smola , Arthur Gretton , Bernhard Schölkopf, Tailoring density estimation via reproducing kernel moment matching, Proceedings of the 25th international conference on Machine learning, p.992-999, July 05-09, 2008, Helsinki, Finland
	Kurt A. Kramer , Lawrence O. Hall , Dmitry B. Goldgof , Andrew Remsen , Tong Luo, Fast support vector machines for continuous data, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, v.39 n.4, p.989-1001, August 2009