Issue No. 08 - August (1998 vol. 20)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/34.709614
<p><b>Abstract</b>—Algorithmic enhancements are described that enable large computational reduction in mean square-error data clustering. These improvements are incorporated into a parallel data-clustering tool, P-CLUSTER, designed to execute on a network of workstations. Experiments involving the unsupervised segmentation of standard texture images were performed. For some data sets, a 96 percent reduction in computation was achieved.</p>
Data clustering, mean square error, data mining, image segmentation, parallel algorithm, network of workstations.
P. K. McKinley, D. Judd and A. K. Jain, "Large-Scale Parallel Data Clustering," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 20, no. , pp. 871-876, 1998.