%0 Journal Article
%T Analyzing and mining ordered information tables<br>Analyzing and Mining Ordered Information Tables
%A Ying Sai
%A Y Y Yao
%A <br>赛英
%J 计算机科学技术学报
%D 2003
%I 
%X Work in inductive learning has mostly been concentrated on classifying. However, there are many applications in which it is desirable to order rather than to classify instances. For modelling ordering problems, we generalize the notion of information tables to ordered information tables by adding order relations in attribute values. Then we propose a data analysis model by analyzing the dependency of attributes to describe the properties of ordered information tables. The problem of mining ordering rules is formulated as finding association between orderings of attribute values and the overall ordering of objects. An ordering rules may state that "if the value of an object x on an attribute a is ordered ahead of the value of another object y on the same attribute, then x is ordered ahead of y". For mining ordering rules, we first transform an ordered information table into a binary information table, and then apply any standard machine learning and data mining algorithms. As an illustration, we analyze in detail Maclean's universities ranking for the year 2000.
%K information tables
%K learning
%K ordering rules
%K ranking
%K rough sets<br>数据表
%K 排序规则
%K 粗设置
%K 归纳学习
%K 关联规则
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=F57FEF5FAEE544283F43708D560ABF1B&aid=5682646A04EA5AF9CF406B8F92E18DC0&yid=D43C4A19B2EE3C0A&vid=13553B2D12F347E8&iid=B31275AF3241DB2D&sid=2B25C5E62F83A049&eid=2B25C5E62F83A049&journal_id=1000-9000&journal_name=计算机科学技术学报&referenced_num=0&reference_num=18