摘要
随着数据量的不断增加,传统的数据处理方法已经无法满足现代大数据处理的需求。近年来,云计算作为一种新型的数据处理方法逐渐被广泛采用。在云计算背景下,K-means聚类算法是一个重要的数据挖掘工具,拥有广泛的应用场景,包括图像处理、文本分析等。但是,当数据量大到一定程度时,传统的K-means聚类算法存在计算效率低和内存占用过大的问题。文章介绍了一种基于云计算的并行K-means聚类算法设计方案,介绍了云计算的概念、云平台技术的应用、云计算平台对并行计算的支持。实验结果表明,K-means算法在处理大规模数据集时的运行时间较长,而采用云计算平台进行并行化计算可以有效提高算法的运行效率。
With the increasing amount of data,traditional data processing methods can no longer meet the needs of modern Big data processing.In recent years,cloud computing has gradually been widely adopted as a new data processing method.In the context of cloud computing,K-means clustering algorithm is an important data mining tool with a wide range of application scenarios,including image processing,text analysis,and so on.However,when the amount of data reaches a certain level,traditional K-means clustering algorithms suffer from low computational efficiency and excessive memory usage.This paper,we introduce a parallel K-means clustering algorithm design scheme based on cloud computing.In the research,the concept of cloud computing and the application of cloud platform technology,and the support of cloud computing platform for parallel computing are introduced.The experimental results show that the K-means algorithm has a long running time when processing large-scale datasets,so using the cloud computing platform can effectively improve the running efficiency of the algorithm.
作者
韩立涛
HAN Litao(Jilin Institute of Technology,Changchun Jilin 130507,China)
出处
《信息与电脑》
2023年第9期93-95,共3页
Information & Computer
基金
吉林省高教科研课题“基于OBE和CIPP模式的单片机原理课程混合式教学模式研究”(项目编号:JGJX2022D423)。
