Abstract
Authors propose parallel greedy heuristic k-means clustering algorithms for implementation on the graphical processing units (GPU) for solving large-scale problems. The computational experiments illustrate high performance of the GPUs in comparison with running the greedy heuristic algorithms on a central processor unit which is especially significant in the case of big datasets and bug numbers of clusters. The efficiency of the greedy heuristic algorithms in comparison with the standard k-means algorithm remains.
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