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Clustering on a Hypercube Multicomputer
April 1991 (vol. 2 no. 2)
pp. 129-137

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.

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Index Terms:
Index Termssquare error; hypercube multicomputer; single-instruction multiple-data; SIMD; clustering problem; NMK processors; multiple-instruction, multiple-data; MIMD; computational complexity; hypercube networks; parallel algorithms
Citation:
S. Ranka, S. Sahni, "Clustering on a Hypercube Multicomputer," IEEE Transactions on Parallel and Distributed Systems, vol. 2, no. 2, pp. 129-137, April 1991, doi:10.1109/71.89059
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