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K-means clustering algorithm is an excellent algorithm which has been widely used in the image processing and data mining. However, the algorithm arouses a high computational complexity. This paper made a parallel analysis of K-means algorithm in detail, and proposed a partitioning and parallel K-means algorithm based on CUDA (Compute unified device architecture). In addition, some optimization strategies, e.g., coalesced memory access, parallel reduction, load balance and instruction optimization, were discussed to obtain the higher performance. Experimental results show that the parallel K-means algorithm achieves 560x speedup over the sequential C codes, while maintains the same effect. Hence it solves the bottleneck of the algorithm perfectly, which is an attractive alternative to the sequential K-means algorithm for image segmentation and clustering analysis.