Sublinear time approximate clustering

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Sublinear time approximate clustering

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Symposium on Discrete Algorithms archive
Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms table of contents

Washington, D.C., United States

Pages: 439 - 447

Year of Publication: 2001

ISBN:0-89871-490-7

Authors

Nina Mishra	Hewlett-Packard Labs, Palo Alto, CA
Dan Oblinger	IBM TJ Watson Labs
Leonard Pitt	University of Illinois at Urbana-Champaign, Urbana, IL

Sponsors

SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIAM : Society for Industrial and Applied Mathematics

Publisher

Society for Industrial and Applied Mathematics Philadelphia, PA, USA

Bibliometrics

Downloads (6 Weeks): 3, Downloads (12 Months): 39, Citation Count: 23

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ABSTRACT

Clustering is of central importance in a number of disciplines including Machine Learning, Statistics, and Data Mining. This paper has two foci: (1) It describes how existing algorithms for clustering can benefit from simple sampling techniques arising from work in statistics [Pol84]. (2) It motivates and introduces a new model of clustering that is in the spirit of the “PAC (probably approximately correct)” learning model, and gives examples of efficient PAC-clustering algorithms.

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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	Sudipto Guha , Adam Meyerson , Nina Mishra , Rajeev Motwani , Liadan O'Callaghan, Clustering Data Streams: Theory and Practice, IEEE Transactions on Knowledge and Data Engineering, v.15 n.3, p.515-528, March 2003
	Artur Czumaj , Christian Sohler, Estimating the weight of metric minimum spanning trees in sublinear-time, Proceedings of the thirty-sixth annual ACM symposium on Theory of computing, June 13-16, 2004, Chicago, IL, USA
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