一种新的基于RS和NN的混合数据挖掘算法

提出一种结合粗糙集理论和BP神经网络理论的新数据挖掘算法.算法利用粗糙集对属性的归约功能将数据仓库中的数据进行归约,将归约后的数据作为训练数据提供给神经网络.通过粗糙集归约,提高了训练数据表达的清晰度,也减少了神经网络的规模,同时利用神经网络又弥补了粗糙集对噪声数据敏感的不足.

A Novel Data Mining Algorithm Based on RS and NN

According to the advantages and the problems existing in rough sets theory and neural network of data mining,an algorithm based on the combination of rough sets theory and BP neural network are presented.This algorithm deducts data from data warehouse by using rough sets' deduct function,and then transfers the deducted data to the BP neural network as training data.By data deduct,the expression of training will become clearer,and the scale of neural network can be simplified.At the same time,neural network can solve rough set's problem of noise sensitivity.This paper also presents a cost function to express the relationship between the amount of training data and the precision of neural network,and to supply the standard for the change from rough set deduct to neural network training.