Sublinear time approximate clustering

Nina Mishra, Daniel Oblinger, Leonard Pitt

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.