%0 Journal Article
%T Multi-variable decision tree construction algorithm based on rough set and entropy<br>基于粗集和熵的多变量决策树的构造算法
%A LUO Qiu-jin
%A MA Rui
%A <br>罗秋瑾
%A 马锐
%J 计算机应用
%D 2007
%I 
%X Multi-variable decision tree is effectively used in the classification of data mining.The key to building lies in the reasonable choice of attributes combination based on the interconnection between attributes.The traditional method is achieved by the relative core of attributes,which is incomplete.A new alternation algorithm was offered.In this algorithm,the number of attributes contained by each node was restricted,and then,attributes combination was selected according to the redefined attribute dependability and the distance function based on condition entropy.In the end,an example was given to show that the method decreases the height of tree,and increases the explanation of classification.
%K rough set
%K multi-variable decision
%K attribute dependability
%K condition entropy
%K distance function<br>粗糙集
%K 多变量决策
%K 属性依赖度
%K 条件熵
%K 距离函数
%K 粗集
%K 多变量决策树
%K 构造算法
%K entropy
%K rough
%K set
%K based
%K algorithm
%K construction
%K 可读性
%K 高度
%K 属性选择
%K 距离函数
%K 条件熵
%K 属性依赖度
%K 重新定义
%K 存在
%K 检验
%K 相对核
%K 挖掘方法
%K 节点
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=831E194C147C78FAAFCC50BC7ADD1732&aid=14E16CBD274305126F8F19D3F615A6C8&yid=A732AF04DDA03BB3&vid=DB817633AA4F79B9&iid=DF92D298D3FF1E6E&sid=387FB6C3BA4B6547&eid=7385F0F53E198CDC&journal_id=1001-9081&journal_name=计算机应用&referenced_num=0&reference_num=7