Abstract
Algorithm k-means is useful for grouping operations; however, when is applied to large amounts of data, its computational cost is high. This research propose an optimization of k-means algorithm by using parallelization techniques and synchronization, which is applied to image segmentation. In the results obtained, the parallel k-means algorithm, improvement 50% to the algorithm sequential k-means. General Terms: speedup
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