%0 Journal Article
%T Wavelet Synopsis Based Clustering of Parallel Data Streams<br>基于小波概要的并行数据流聚类
%A CHEN Hua-Hui
%A SHI Bai-Le
%A QIAN Jiang-Bo
%A CHEN Ye-Fang
%A <br>陈华辉
%A 施伯乐
%A 钱江波
%A 陈叶芳
%J 软件学报
%D 2010
%I 
%X In many real-life applications, such as stock markets, network monitoring, and sensor networks, data are modeled as dynamic evolving time series which is continuous and unbounded in nature, and many such data streams concur usually. Clustering is useful in analyzing such paralleled data streams. This paper is interested in grouping these evolving data streams. For this purpose, a synopsis is maintained dynamically for each data stream. The construction of the synopsis is based on Discrete Wavelet Transform and utilizes the amnesic feature of data stream. By using the synopsis, a fast computation of approximate distances between streams and the cluster center can be implemented, and an efficient online version of the classical K-means clustering algorithm is developed. Experiments have proved the effectiveness of the proposed method.
%K clustering
%K synopsis
%K amnesic feature
%K discrete wavelet transform
%K data stream<br>聚类
%K 概要
%K 遗忘特性
%K 离散小波变换
%K 数据流
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=7735F413D429542E610B3D6AC0D5EC59&aid=8EA56788FCC676C3906C83B69365C476&yid=140ECF96957D60B2&vid=659D3B06EBF534A7&iid=E158A972A605785F&sid=0636354D8CF77519&eid=4E8E6A5CE04FD382&journal_id=1000-9825&journal_name=软件学报&referenced_num=0&reference_num=20