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Clustering on a hypercube multicomputer

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2 Author(s)
Ranka, S. ; Sch. of Comput. Sci., Syracuse Univ., NY, USA ; Sahni, S.

Squared error clustering algorithms for single-instruction multiple-data (SIMD) hypercubes are presented. The algorithms are shown to be asymptotically faster than previously known algorithms and require less memory per processing element (PE). For a clustering problem with N patterns, M features per pattern, and K clusters, the algorithms complete in O(k+log NM ) steps on NM processor hypercubes. This is optimal up to a constant factor. These results are extended to the case in which NMK processors are available. Experimental results from a multiple-instruction, multiple-data (MIMD) medium-grain hypercube are also presented

Published in:

Parallel and Distributed Systems, IEEE Transactions on  (Volume:2 ,  Issue: 2 )

Date of Publication:

Apr 1991

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