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Large-scale parallel data clustering

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3 Author(s)
Judd, D. ; Dept. of Comput. Sci., Michigan State Univ., East Lansing, MI, USA ; McKinley, P.K. ; Jain, A.K.

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 512×512 image at approximately the same computational cost as that of previous methods applied to only 5 percent of the image pixels

Published in:

Pattern Recognition, 1996., Proceedings of the 13th International Conference on  (Volume:4 )

Date of Conference:

25-29 Aug 1996

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