Large-scale parallel data clustering

Abstract

Algorithmic enhancements are described that allow large reduction (for some data sets, over 95 percent) in the number of floating point operations in mean square error data clustering. These improvements are incorporated into a parallel data clustering tool, P-CLUSTER, developed in an earlier study. Experiments on segmenting standard texture images show that the proposed enhancements enable clustering of an entire 512/spl times/512 image at approximately the same computational cost as that of previous methods applied to only 5 percent of the image pixels.