%0 Journal Article
%T Text Classification Based on Maximal Association Rule<br>基于最大关联规则的文本分类
%A HE Yu
%A FENG Jian-Lin
%A WANG Yuan-Zhen
%A <br>何玉
%A 冯剑琳
%A 王元珍
%J 计算机科学
%D 2006
%I 
%X We propose a novel association based method called SAT-MOD for text classification. While previous methods mainly mined frequently co-occurring words (frequent itemsets) at the document-level, the basic semantic unit in a document is a sentence. Words within the same sentence are typically more semantically related than words that appear in the same document. Our proposed SAT-MOD views a sentence rather than a document as a transaction. The effectiveness of proposed SAT-MOD method has been demonstrated by extensive experimental studies using popular benchmark text collections.
%K Text classification
%K Association rules
%K Maximal frequent itemsets<br>文本分类
%K 关联规则
%K 最大频繁项目集
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=64A12D73428C8B8DBFB978D04DFEB3C1&aid=CBC4CDF6A866819C&yid=37904DC365DD7266&vid=27746BCEEE58E9DC&iid=708DD6B15D2464E8&sid=475189FCB44F11F6&eid=769BD58726D66E7D&journal_id=1002-137X&journal_name=计算机科学&referenced_num=0&reference_num=8