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A FAST k-MEANS IMPLEMENTATION USING CORESETS
2008
International journal of computational geometry and applications
In this paper we develop an efficient implementation for a k-means clustering algorithm. The novel feature of our algorithm is that it uses coresets to speed up the algorithm. A coreset is a small weighted set of points that approximates the original point set with respect to the considered problem. The main strength of the algorithm is that it can quickly determine clusterings of the same point set for many values of k. This is necessary in many applications, since, typically, one does not
doi:10.1142/s0218195908002787
fatcat:zxljewv2rvgh5p7eco3wrxz5te