k-means算法的并行化.doc

陈涛一种基于“云”计算平台的并行聚类— K-means 算法设计与实现 I 题目: 一种基于“云”计算平台的并行聚类—— K-means 算法设计与实现陈涛一种基于“云”计算平台的并行聚类— K-means 算法设计与实现 II 摘要云计算(puting) 是分布式计算(puting) 、并行计算(puting) 和网格计算(puting) 的发展, 云计算是一种新兴的分布式并行计算环境或模式, 云计算的出现使得数据挖掘技术的网络化和服务化将成为新的趋势。本文是对并行聚类算法 K-means 的研究。首先介绍了 K-means 算法在单个计算机上的聚类算法的设计思想, 其次重点对 K-mean s 算法在集群环境下聚类算法的设计思想进行具体阐述。 K-means 聚类算法在面对海量数据时, 时间和空间的复杂性已成为 K-means 聚类算法的瓶颈。本文在充分研究传统 K-Mean s 聚类算法的基础上, 提出了基于的并行 K-Mean s 聚类算法的设计思想, 给出了其加速比估算公式。并通过实验证明了该算法的正确性和有效性。关键字: K-means ;并行;聚类;集群环境陈涛一种基于“云”计算平台的并行聚类— K-means 算法设计与实现 III Abstract puting , which isa nascent distribu ted puting environment or pattern , is the development of puting, puting and puting . The appearance of puting makes work and the service of the data mining technology e a new trend. The paper isa study of K-means which is among the parallel clustering algorithms. Firstly, it illustrates the design ideology of clustering algorithm of K-means algorithm on the every puter. Secondly, it mainly elaborates the design ideology of K-means algorithm of clustering algorithms working in the clustering environment. Being confronted with a large quantity of data, plexity of time and space has been the bottleneck of K-means. Based on the sufficient studies of traditional K-means, the paper puts forward the design ideology on the basis of the parallel K-means clustering algorithms and provides its estimation formula of speed-up ratio . The paper also proves the accuracy and the effectiveness of this algorithm by the means of the experiments. Key Words: K-means ; Parallel; Clustering; Cluster environment 陈涛一种基于“云”计算平台的并行聚类— K-means 算法设计与实现 IV 目录摘要....................................................................................................................... I Abstract ................................................................................................................. II 目录................................................................................................................... III 1 引言