%0 Journal Article
%T Method based on rough set for mining multi-dimensional association rules<br>基于粗糙集的多维关联规则挖掘方法
%A TAO Duo-xiu
%A LV Yue-jin
%A DENG Chun-yan
%A <br>陶多秀
%A 吕跃进
%A 邓春燕
%J 计算机应用
%D 2009
%I 
%X It is very time-consuming to discover association rules from the mass of data, and not all the rules are attractive to the user, so a lot of irrelevant information to the user's requirements may be generated when traditional mining methods are applied. In addition, most of the existing algorithms are for discovering one-dimensional association rules. Therefore, the authors defined a mining language which allowed users to specify items of interest to the association rules, as well as the parameters (for example, support, confidence, etc.). A method based on rough set theory for multi-dimensional association rules mining was also proposed, which dynamically generated frequent item sets and multi-dimensional association rules, and reduced the search space to generate frequent item sets. Finally, an example verifies the feasibility and effectiveness of the method.
%K association rule
%K multi-dimensional association rules
%K frequent item sets
%K rough set<br>关联规则
%K 多维关联规则
%K 频繁集
%K 粗糙集
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=831E194C147C78FAAFCC50BC7ADD1732&aid=EEFA8F1C9EF7171EEC7BCF74319B7C26&yid=DE12191FBD62783C&vid=771469D9D58C34FF&iid=94C357A881DFC066&sid=B754280783395EEA&eid=8F4C67DCFE6D499D&journal_id=1001-9081&journal_name=计算机应用&referenced_num=1&reference_num=17