%0 Journal Article
%T Learning Bayesian network equivalence classes based on mutual information<br>基于互信息学习贝叶斯网络等价类*
%A LI Bing-han
%A GAO Xiao-li
%A LIU San-yang
%A <br>李冰寒
%A 高晓利
%A 刘三阳
%J 计算机应用研究
%D 2011
%I 
%X Constructing Bayesian network structures from data is NP-hard.According to the mutual information and conditional independence test,this paper presented a new algorithm for the construction of the optimal Bayesian network structure.Numerical experiments show that the new algorithm can determined much faster the structure with highest degree of data matching,thus the study of Bayesian network structures become more efficient.
%K data mining
%K Bayesian network
%K structure learning
%K connected graph
%K mutual information
%K conditional independence test<br>数据挖掘
%K 贝叶斯网络
%K 结构学习
%K 连通图
%K 互信息
%K 条件独立测试
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=1F92055A3E9058B30939A278BF237EAD&yid=9377ED8094509821&vid=D3E34374A0D77D7F&iid=CA4FD0336C81A37A&sid=35FC3610259C2B32&eid=06EA2770E96C5402&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=19