作　　者： ; ;

机构地区： 辽宁工程技术大学电子与信息工程学院

出　　处： 《计算机应用》 2011年第5期1363-1366,共4页

摘　　要： 为了提高数据流的聚类质量和效率,采用等时间跨度滑动窗口技术,然后利用改进的微簇结构保存数据流的概要信息,最后利用微簇删除策略,定期删除过期、孤立微簇。基于真实数据集与人工数据集的实验表明:与传统基于界标模型的聚类算法相比,该算法可获得较好的效率、较小的内存开销和快速的数据处理能力。 Stream data clustering algorithm was improved in terms of cluster quality and efficiency.This paper adopted a new method to improve cluster quality and efficiency.Firstly,the technology of the time-based sliding window was applied.Secondly,the structure of improved micro-cluster was created to save the summary.Finally,a new strategy was designed to regularly delete expired micro-clusters and outlier micro-clusters.Compared with traditional clustering algorithms of landmark-based model,the proposed method is of better efficiency,less memory overhead and fast data processing capabilities.

关 键 词： 数据流 聚类 滑动窗口 微簇 界标模型

领　　域： [自动化与计算机技术] [自动化与计算机技术]