国产欧美精品一区二区,中文字幕专区在线亚洲,国产精品美女网站在线观看,艾秋果冻传媒2021精品,在线免费一区二区,久久久久久青草大香综合精品,日韩美aaa特级毛片,欧美成人精品午夜免费影视

Traditional K-Medoids clustering algorithm selects the initial points randomly, which is easy to produce local optimum; when replace the cluster centers, adopted global sequential replacement policy reduces the efficiency of the algorithm; at the same time, it is difficult to adapt to operation of massive data. In response to the above problems, an improved K-Medoids clustering algorithm in cloud environment is proposed. The algorithm combines the density method and Max-Min principle to obtain optimized cluster centers, and replaces centers in the area of Canopy, and adopts optimization criterion function, and finally uses the ideas of sequential composition of MapReduce programming model to achieve the parallel extensions of the algorithm. Result of the experiments shows that the improved method is less dependent on the initial points and reduces the number of iterations and the clustering time.