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Clustering on a hypercube multicomputer

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2 Author(s)
Ranka, S. ; Syracuse Univ., NY, USA ; Sahni, S.

Squared-error clustering algorithms for single-instruction multiple-data (SIMD) hypercubes are presented. These algorithms are asymptotically faster than previous algorithms and require less memory per processing element. For a clustering problem with N patterns, M features per pattern, and K clusters, the algorithms complete it in O(K+log NM) steps on NM processor hypercubes. This is optimal up to a constant factor. Experimental results from a commercially available multiple-instruction multiple-data (MIMD) medium-grain hypercube show that the clustering problem can be solved efficiently by the machines

Published in:

Pattern Recognition, 1990. Proceedings., 10th International Conference on  (Volume:ii )

Date of Conference:

16-21 Jun 1990