%0 Journal Article
%T Convergent Network Approach for Rule Extraction in KDD and Its Applications<br>KDD中规则提取的收敛网络方法及其应用
%A XIONG Fan-lun
%A DENG Chao
%A <br>熊范纶
%A 邓超
%J 软件学报
%D 2000
%I 
%X A novel neural network based rule extraction method is proposed in this paper. This method consists of a primary network and its corresponding mapping network, which includes twice convergent processes. The knowledge acquisition and network construction of the method are fulfilled by the first convergence of the primary network. Here by a mapping network corresponding to the converged primary network is created whose convergence is capable of realizing the rule extraction. Since there is no need of enumerating the overall space of solutions for this method to extract rules, therefore the searching efficiency is greatly increased and the computation complexity is dramatically reduced. Meanwhile, a stop criterion of rule extraction in terms of difference of belief degree is also proposed in this paper. A lot of simulation experiments and practical applications illustrate and verify the validity and correctness of the proposed method.
%K KDD (knowledge discovery and data mining)
%K rule extraction
%K neural network
%K convergent network
%K difference of belief degree<br>KDD(knowledge
%K discovery
%K and
%K data
%K mining)
%K 规则提取
%K 神经网络
%K 收敛网络
%K 信度差
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=7735F413D429542E610B3D6AC0D5EC59&aid=319A122A2F4D75A3&yid=9806D0D4EAA9BED3&vid=708DD6B15D2464E8&iid=59906B3B2830C2C5&sid=D9BD5E184C6E559D&eid=EDB2C8F3B92E3D13&journal_id=1000-9825&journal_name=软件学报&referenced_num=0&reference_num=7