面向大数据的K-means算法综述

聚类作为一种重要的数据挖掘方式，如何在海量数据下更快获得一个有理论保证的K-means的近似解则是一个关键问题。首先，定义K-means问题并介绍相关背景；然后，从理论保证和加速两个方面分别介绍国内外先进研究成果；最后，总结现有的成果并对未来的面向大数据的K-means研究方向予以展望和预测。

Survey of K-means algorithms on big data

Among all the clustering problems, the K-means problem is probably the most well-known one. How to obtain a theoretically guaranteed solution of K-means efficiently for the big data can be a key problem. This paper surveyed the progress of this problem. Firstly, this paper defined the K-means problem and introduced relevant backgrounds. Secondly, it introduced separately and described in details the techniques for theoretical guarantee and speed up. Finally, it summarized the main results and forecasted the future directions of K-means algorithms on big data.