计算机技术与发展2013，Vol.23Issue(7)：87-91,5.DOI:10.3969/j.issn.1673-629X.2013.07.022
基于RBF神经网络与粗糙集的数据挖掘算法
Data Mining Algorithm Based on RBF Neural Network and Rough Sets
摘要
Abstract
With the rise of data mining technology,in order to improve the accuracy of data mining,a lot of data mining algorithms have been put forward.The data mining algorithm which combinesneural networks with rough set theory has been one of the hot spots of data mining research based on rough set theory.Put forward the new idea of training data firstly,then pass to rough sets data mining after diminating interference data,take the advantages of Radical Basis Function (RBF) neural network:fast convergence rate and strong generalization capability etc.And through the contrast to the data mining results which not using RBF neural network training,the precision of the algorithm which combined RBF neural network with rough set is greatly improved,it shows that the data mining algorithm which combined with neural network and rough sets theory has validity and feasibility.关键词
RBF神经网络/粗糙集/数据挖掘Key words
RBF neural network/ rough sets/ data mining分类
信息技术与安全科学引用本文复制引用
储兵,吴陈,杨习贝..基于RBF神经网络与粗糙集的数据挖掘算法[J].计算机技术与发展,2013,23(7):87-91,5.基金项目
国家自然科学基金资助项目(61100116/F020512) （61100116/F020512）
