Distributed data clustering can be efficient and exact

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Distributed data clustering can be efficient and exact

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ACM SIGKDD Explorations Newsletter archive
Volume 2 , Issue 2 (December 2000) table of contents
Special issue on “Scalable data mining algorithms”

Pages: 34 - 38

Year of Publication: 2000

ISSN:1931-0145

Authors

George Forman	Hewlett-Packard Research Labs., 1501 Page Mill, Palo Alto, CA
Bin Zhang	Hewlett-Packard Research Labs., 1501 Page Mill, Palo Alto, CA

Publisher

ACM New York, NY, USA

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Downloads (6 Weeks): 6, Downloads (12 Months): 82, Citation Count: 7

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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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	8	[KC99] Kantabutra, S. and Couch, A. L., "Parallel K-Means Clustering Algorithm on NOWs," NECTEC Technical Journal, Vol. 1, No. l, March 1999.
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	11	[MK97] McLachlan, G. J. and Krishnan, T., "The EM Algorithm and Extensions," John Wiley & Sons, Inc., 1997.
	12	[NetPerception] A commercial recommender system, http://www.netperceptions.com
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	14	Lawrence Snyder, A programmer's guide to ZPL, MIT Press, Cambridge, MA, 1999
	15	Bin Zhang , Meichun Hsu , Umeshwar Dayal, K-Harmonic Means - A Spatial Clustering Algorithm with Boosting, Proceedings of the First International Workshop on Temporal, Spatial, and Spatio-Temporal Data Mining-Revised Papers, p.31-45, September 12, 2000
	16	[Z00b] Zhang, B. "Generalized K-Harmonic Means - Boosting in Unsupervised Learning", Hewllet-Packard Laboratories Technical Report: http://www.hpl.hp.com/techreports/2000/HPL- 2000-137.html.
	17	Bin Zhang , Meichun Hsu , George Forman, Accurate Recasting of Parameter Estimation Algorithms Using Sufficient Statistics for Efficient Parallel Speed-Up: Demonstrated for Center-Based Data Clustering Algorithms, Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery, p.243-254, September 13-16, 2000
	18	Tian Zhang , Raghu Ramakrishnan , Miron Livny, BIRCH: an efficient data clustering method for very large databases, ACM SIGMOD Record, v.25 n.2, p.103-114, June 1996

CITED BY 7

	Ruoming Jin , Ge Yang , Gagan Agrawal, Shared Memory Parallelization of Data Mining Algorithms: Techniques, Programming Interface, and Performance, IEEE Transactions on Knowledge and Data Engineering, v.17 n.1, p.71-89, January 2005
	Tao Li , Shenghuo Zhu , Mitsunori Ogihara, Algorithms for clustering high dimensional and distributed data, Intelligent Data Analysis, v.7 n.4, p.305-326, September 2003
	Debzani Deb , M. Muztaba Fuad , Rafal A. Angryk, Distributed hierarchical document clustering, Proceedings of the 2nd IASTED international conference on Advances in computer science and technology, p.328-333, January 23-25, 2006, Puerto Vallarta, Mexico
	Mei Li , Guanling Lee , Wang-Chien Lee , Anand Sivasubramaniam, PENS: an algorithm for density-based clustering in peer-to-peer systems, Proceedings of the 1st international conference on Scalable information systems, p.39-es, May 30-June 01, 2006, Hong Kong
	Valerie Fiolet , Bernard Toursel, A clustering method to distribute a database on a grid, Future Generation Computer Systems, v.23 n.8, p.997-1002, November, 2007
	Ron Bekkerman , Martin Scholz, Data weaving: scaling up the state-of-the-art in data clustering, Proceeding of the 17th ACM conference on Information and knowledge management, October 26-30, 2008, Napa Valley, California, USA
	R. Chandrasekar , T. Srinivasan, An improved probabilistic ant based clustering for distributed databases, Proceedings of the 20th international joint conference on Artifical intelligence, p.2701-2706, January 06-12, 2007, Hyderabad, India