Sublinear time approximate clustering

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 bene t 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 e cient PAC-clustering algorithms.

• Type: Conference
• Year: 2001
• Where: SODA
• Authors: Nina Mishra, Daniel Oblinger, Leonard Pitt
• Keywords: Algorithms, Central Importance, Probably Approximately Correct, Simple Sampling Techniques, SODA 2001