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Issue No.08 - August (1998 vol.20)
pp: 871-876
<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.
Dan Judd, Philip K. McKinley, Anil K. Jain, "Large-Scale Parallel Data Clustering", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.20, no. 8, pp. 871-876, August 1998, doi:10.1109/34.709614
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