%0 Journal Article
%T Study for Mining Maximally Frequent Item Sets in Association Rule<br>关联规则中最大频繁项目集的研究
%A LI Qing-feng
%A YANG Lu-ming
%A ZHANG Xiao-feng
%A <br>李清峰
%A 杨路明
%A 张晓峰
%J 计算机应用研究
%D 2005
%I 
%X Study the frequent item sets problem for association rule in large business database; propose an efficient new algorithm MMFI in mining maximum frequent item sets. The idea of MMFI is to divide database by level and divide candidate frequent item sets by number. This algorithm is proved that it is efficient to reduce time in compute by mathematics and expriments.
%K Data Mining
%K Association Rules
%K Maximum Frequent Item Sets
%K Scanning Database Method
%K Frequent Pattern Free Method<br>数据挖掘
%K 关联规则
%K 最大频繁集
%K 数据库扫描法
%K 频繁树法
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=1EAEF2A46BE12D80&yid=2DD7160C83D0ACED&vid=BC12EA701C895178&iid=CA4FD0336C81A37A&sid=39EEF47180459690&eid=C36EC077A8A90308&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=4&reference_num=18