A fast k-means implementation using coresets

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 know a good value for k in advance. Once we have clusterings for many different values of k we can determine a good choice of k using a quality measure of clusterings that is independent of k, for example the average silhouette coefficient. The average silhouette coefficient can be approximated using coresets. To evaluate the performance of our algorithm we compare it with algorithm KMHybrid [28] on typical 3D data sets for an image compression application and on artificially created instances....

• Type: Conference
• Year: 2006
• Where: COMPGEOM
• Authors: Gereon Frahling, Christian Sohler
• Keywords: Algorithm, Average Silhouette Coefficient, COMPGEOM 2006, K-means Clustering Algorithm