计算机工程2012，Vol.38Issue(13)：145-147,151,4.DOI:10.3969/j.issn.1000-3428.2012.13.043
分批处理的K-means算法并行实现
Parallel Implementation of K-means Algorithm with Batch Processing
摘要
Abstract
K-means algorithm is computationally intensive, time consuming and convergence slow. In order to solve the problem of K-means algorithm, a new set of parallel solution of K-means algorithm is presented. In the General Purpose computation on Graphics Processing Unit(GPGPU) architecture, Compute Unified Device Architecture(CUDA) is used to accelerate K-means algorithm. Based on batch principle, the algorithm uses CUDA's memory more rationally, to avoid access conflict, reduce the number of times of visits for data sets, and improve the efficiency of K-means algorithm. Experimental result in large-scale data set shows that the algorithm has a faster clustering speed.关键词
数据挖掘/K-means算法/统一计算设备架构/并行算法/聚类分析/图形处理器Key words
data mining/ K-means algorithm/ Compute Unified Device Architecture(CUDA)/ parallel algorithm/ clustering analysis/ Graphics Processing Unit(GPU)分类
信息技术与安全科学引用本文复制引用
兰远东,刘宇芳,徐涛..分批处理的K-means算法并行实现[J].计算机工程,2012,38(13):145-147,151,4.基金项目
国家“863”先进制造领域基金资助重点项目(2006AA04A120) （2006AA04A120）
广东高校优秀青年创新人才培养计划基金资助项目(LYM09128) （LYM09128）
