Large-Scale Parallel Data Clustering

  • Authors:
  • Dan Judd;Philip K. McKinley;Anil K. Jain

  • Affiliations:
  • Michigan State Univ., East Lansing;Michigan State Univ., East Lansing;Michigan State Univ., East Lansing

  • Venue:
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Year:
  • 1998

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Abstract

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.