关键词: K-means, 随机抽样, 海量数据, MapReduce

Abstract: The K-means algorithm when dealing with massive data, is easy to bring the phenomenon of memory overflow. Although this problem is solved by using the MapReduce framework to improve K-means, the phenomenon clustering effect is not so stable and the accuracy is not so high. It is necessary to raise an improved algorithm, which uses MapReduce framework to implement the K-means, by means of random sampling, calculating density, distance and the square difference. Finally, it selects the best initial cluster center and adopts the new method of center point calculation in the iteration. Experimental results show that, the improved algorithm has good stability， accuracy and accelerating ratio.

Key words: K-means, random sampling, massive data, MapReduce