Large-Scale Parallel Data Clustering

—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.

• Type: Journal
• Year: 1998
• Where: PAMI
• Authors: Dan Judd, Philip K. McKinley, Anil K. Jain
• Keywords: Large Computational Reduction, PAMI 1998, Parallel Data-clustering Tool, —Algorithmic Enhancements