粗糙集和神经网络方法在数据挖掘中的应用  

Application of rough set and neural network in data mining

作　　者：周序生[1] 王志明[2] 

机构地区：[1]湖南工业大学,湖南株洲412008 [2]怀化职业技术学院,湖南怀化415000

出　　处：《计算机工程与应用》2009年第7期146-149,共4页Computer Engineering and Applications

摘　　要：提出了一种基于神经网络和粗集的数据挖掘新方法。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,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。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 networks classification,robustness ability.Experimental results show that this algorithm can produce more effective and simpler rules quickly and possesses good robustness.

关 键 词：数据挖掘 粗集理论 神经网络 分类 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]