@inproceedings{a85ef4cd6e2841c9a0d6b6f27c256864,

title = "Sublinear time approximate clustering",

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.",

keywords = "Algorithms, Measurement, Performance, Theory, Verification",

author = "Nina Mishra and Dan Oblinger and Pitt, {Leonard B}",

year = "2001",

language = "English (US)",

isbn = "0898714907",

series = "Proceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms",

pages = "439--447",

booktitle = "Proceedings of the 12th Annual ACM-SIAM Symposium on Discrete Algorithms",

note = "2001 Operating Section Proceedings, American Gas Association ; Conference date: 30-04-2001 Through 01-05-2001",

}