%0 Journal Article
%T Mining network traffic frequent itemsets with sliding-time-fading window<br>挖掘滑动时间衰减窗口中网络流频繁项集*
%A LAI Jun
%A LI Shuang-qing
%A <br>赖军
%A 李双庆
%J 计算机应用研究
%D 2011
%I 
%X Mining network traffic frequent itemsets is an important foundation for network traffic analysis. A novel algorithm STFWFI(sliding time fading window frequent itemsets) based on lexicographic ordered prefix tree LOP-Tree is proposed. STFWFI uses a sliding-time-fading window model which accords with the characteristic of network traffic, and reduces the computational time complexity and space complexity efficiently. A novel node weight count measure SDNW(statistical distribution node weight)in LOP-Tree structure based on statistical distribution is proposed instead of the conventional statistical count measure, and improves the count precision of network traffic nodes. The experimental results show that STFWFI performs much better than the previous approaches in mining network traffic frequent itemsets.
%K Network traffic mining
%K Frequent itemsets
%K Sliding-time-fading window
%K Lexicographic ordered prefix tree<br>网络流数据挖掘
%K 频繁项集
%K 滑动时间衰减窗口
%K 字典顺序前缀树
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=9FA51CDB76588088B1DDEB7A3EE55C58&yid=9377ED8094509821&vid=D3E34374A0D77D7F&iid=38B194292C032A66&sid=2DEC3FE1EFC628C2&eid=9124D83E61CF1CD0&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=8