计算机工程与应用2011，Vol.47Issue(7)：135-138,4.DOI:10.3778/j.issn.1002-8331.2011.07.039
动态滑动窗口的数据流聚类方法
Approach for data streams clustering over dynamic sliding windows.
摘要
Abstract
The clustering of data streams is an important problem for clustering analysis.In order to address the data streams with varying speed, an efficient data streams clustering algorithm over dynamic sliding windows is proposed, which based on the two-phased framework. In the online component,the novel micro-cluster feature is introduced to store the important statistical information of data streams. Through computing the distances from data points to the center of each micro-cluster,and adjusting the sizes of sliding windows,the corresponding clustering features are maintained dynamically. In the offiine component,by employing the mean values of the micro-clusters in online component,k-means algorithm is adopted to generate the final clustering results. Experimental results show that this approach has higher clustering purity and better scalability.关键词
数据挖掘/数据流/聚类/滑动窗口Key words
data mining/data streams/clustering/sliding windows分类
信息技术与安全科学引用本文复制引用
张忠平,王浩,薛伟,夏炎..动态滑动窗口的数据流聚类方法[J].计算机工程与应用,2011,47(7):135-138,4.基金项目
国家自然科学基金(the National Natural Science Foundation of China under Grant No.60773100) （the National Natural Science Foundation of China under Grant No.60773100）
河北省教育厅科研计划项目(the Scientific Research Project of the Department of Hebei Education of China under Grant No.2006143). （the Scientific Research Project of the Department of Hebei Education of China under Grant No.2006143）
