加速大数据聚类K-means算法的改进

Improved accelerating large data K-means clustering algorithm

为有效处理大规模数据聚类的问题,提出一种先抽样再用最大最小距离进行K-means并行化聚类的方法。基于抽样的方法避免了聚类陷入局部解中,基于最大最小距离法使得初始聚类中心趋于最优化。大量实验结果表明,无论是在单机环境还是集群环境下,该方法受初始聚类中心的影响降低,提高了聚类的准确性,减少了聚类的迭代次数,降低了聚类的时间。

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 num-ber of iterations of clustering and the clustering time.