Spark环境下K-means初始中心点优化研究综述  

Survey of optimization on K-means algorithm in Spark

作　　者：行艳妮 钱育蓉[1] 南方哲 赵京霞[1] Xing Yanni;Qian Yurong;Nan Fangzhe;Zhao Jingxia(College of Software,Xinjiang University,Urumqi 830046,China)

机构地区：[1]新疆大学软件学院,乌鲁木齐830046

出　　处：《计算机应用研究》2020年第3期641-647,共7页Application Research of Computers

基　　金：国家自然科学基金资助项目(61562086,61462079,61966035);新疆维吾尔自治区教育厅创新团队资助项目(XJEDU2016S035);自治区研究生创新项目(XJ2019G072,XJ2019G069,XJ2019G071)。

摘　　要：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。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.

关 键 词：K-均值算法 分布式内存计算框架 算法优化 聚类算法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]