%0 Journal Article
%T Research on similarity measure for time series based on SAX<br>基于SAX的时间序列相似性度量方法*
%A LI Gui-ling
%A WANG Yuan-zhen
%A YANG Lin-quan
%A WU Xiang-ninga
%A <br>李桂玲
%A 王元珍
%A 杨林权
%A 吴湘宁a
%J 计算机应用研究
%D 2012
%I 
%X Symbolic approximation is an effective dimensionality reduction technique for time series, its similarity measure is a basis for various mining tasks. MINDIST_PAA_iSAX is a distance function based on symbolic aggregate approximation (SAX), but it does not satisfy symmetry, so it has limitation in mining time series. This paper put forward and proved a symmetric distance measure Sym_PAA_SAX to be lower bounding to Euclidean distance. Experiments on real and synthetic data sets show its better tightness of lower bounding and lower false positives rate in similarity search.
%K time series
%K dimensionality reduction
%K similarity measure
%K lower bounding<br>时间序列
%K 降维
%K 相似性度量
%K 下界
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=F950C016BC33E901F9BB9F10B4DF405C&yid=99E9153A83D4CB11&vid=771469D9D58C34FF&iid=38B194292C032A66&sid=3DC9CEF02B8360EE&eid=B65E5C7BA0DC04DC&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=14