Spark环境下K-means初始中心点优化研究综述

为了能够及时了解Spark环境下经典聚类算法K-means的最新研究进展,把握K-means算法当前的研究热点和方向,针对K-means算法的初始中心点优化研究进行综述。首先介绍了内存计算框架Spark和K-means算法,并分析了K-means算法聚类不稳定性的成因和影响,其目的在于指出优化K-means算法的重要性;详细介绍了目前在Spark环境下优化K-means初始中心点的主要方法和最新研究现状,并展望了K-means初始中心点优化问题的未来研究方向。

Survey of optimization on K-means algorithm in Spark

In order to understand the latest research progress of the classical clustering algorithm K-means in Spark environment, and grasp the current research hotspots and directions of K-means algorithm, this paper reviewed the initial center point optimization research on K-means algorithm. Firstly, it introduced the memory computing framework Spark and K-means algorithms, and analyzed the cause and effects of clustering instability of K-means algorithm, which pointed out the importance of optimizing K-means algorithm. This paper introduced the main methods and the latest research status of optimizing the initial center point of K-means in Spark environment in detail, and also discussed the future research trends in initial center point optimization of K-means.