%0 Journal Article
%T Chernoff Bound Based Approximate Frequent Itemset Mining Method over Streams<br>一种基于Chernoff Bound的数据流上近似频繁项集的挖掘方法
%A LI Hai-feng
%A ZHANG Ning
%A <br>李海峰
%A 章宁
%J 计算机科学
%D 2011
%I 
%X A data stream is fast, unlimited and dynamic, these characteristics constraint the computational resources and storages when mining frectuent itemsets. This paper addressed this problem and proposed a simple and effective algorithm AFIoDS, AFIoDS is an approximate algorithm based on sliding window model, which splits stream data into batches and maintains them with 2-tuple lists;thus,a false negative result can be obtained using a probabilistic parameterbased on chernoff bound. The approximation will be changed dynamically to guarantee the mining frequent itemsets are error controllable. Plus, a compression of frequent itemsets, the closed frequent itemsets, arc employed to represent the results of each batch for further memory saving. Our experimental results on 3 real world data show that without precision reduction, AFIoDS achieves a faster speed and a much reduced memory cost in comparison with the statcof-thcart algorithms.
%K Chernoff bound
%K Data stream
%K Frequent itemset<br>Chernoff
%K Bound，数据流，频繁项集
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=64A12D73428C8B8DBFB978D04DFEB3C1&aid=E60E8B58E8EFB4834700091D92963E51&yid=9377ED8094509821&vid=16D8618C6164A3ED&iid=94C357A881DFC066&sid=F260CE035846B3B8&eid=BBF7D98F9BEDEC74&journal_id=1002-137X&journal_name=计算机科学&referenced_num=0&reference_num=16