摘要
本文提出了一种基于粗集理论和神经网络的数据挖掘新方法。首先利用粗集理论对原始数据进行一致性属性约简 ,然后使用神经网络对数据进行学习和预测 ,并同时完成属性的不一致约简 ,最后再由粗集对神经网络中的知识进行规则抽取。该方法充分融合了粗集理论强大的属性约简、规则生成能力和神经网络优良的分类、容错能力。实验表明 ,该方法快速有效 ,生成规则简单准确 ,具有良好的鲁棒性。
In this paper,a new method of data mining based on rough set and neural network is proposed. Based on the rough set theory,attribute reduction is processed on data under the consistent conditions. Then neural network is used to study and predict data,at the same time to reduce the attributes under the inconsistent conditions. Finally rule knowledge in the neural network is extracted by using rough set theory. The method mixes rough set's strong attribute reduction,rule extraction ability and neural network's classification,robustness ability. Experimental results show that this algorithm can produce more effective and simpler rules quickly and possesses good robustness.
出处
《情报学报》
CSSCI
北大核心
2002年第6期674-679,共6页
Journal of the China Society for Scientific and Technical Information
基金
国家自然科学基金资助项目 (编号 :6 0 2 75 0 2 0 )
