Improved accelerating large data K-means clustering algorithm

To deal with large-scale data clustering problems,a speeding K-means parallel clustering method was presented which randomly sampled first and then used max-min distance means to carry out K-means parallel clustering.Sampling based method avoids the problem of clustering in local solutions and max-min distance based method makes the initial clustering centers tend to be optimum.Results of a large number of experiments show that the proposed method is affected less by the initial clustering center and improves the precision of clustering in both stand-alone environment and cluster environment.It also reduces the number of iterations of clustering and the clustering time.