Issue No. 02 - April (1991 vol. 2)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/71.89059
<p>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.</p>
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
S. Ranka and S. Sahni, "Clustering on a Hypercube Multicomputer," in IEEE Transactions on Parallel & Distributed Systems, vol. 2, no. , pp. 129-137, 1991.