文献类型：期刊文献

中文题名：粗糙集神经网络算法在数据挖掘中的研究与应用

英文题名：Research of Rough Set Neural Network on DM System

作者：王晓洁[1];王付强[1]

第一作者：王晓洁

机构：[1]新乡师范高等专科学校计算机科学系

第一机构：新乡学院计算机与信息工程学院

年份：2007

卷号：15

期号：4

起止页码：23-25

中文期刊名：河南机电高等专科学校学报

外文期刊名：Journal of Henan Mechanical and Electrical Engineering College

语种：中文

中文关键词：粗糙集;神经网络;数据挖掘;关联规则

外文关键词：rough set ; neural network; data mining; association rules

摘要：神经网络是数据挖掘中最为常用的算法之一。它具有正确率高、抗噪声数据能力强、计算错误率低等优点。但神经网络算法也存在结构相对复杂、训练时间长、计算结果的可解释度比较低等问题。文中采用粗糙集理论对数据进行预处理,使用神经规则进行数据挖掘的新方法,该方法可以在结果精度有限降低的前提下,得到表示简单明确且错误率低的关联规则,同时可以减少网络训练时间,大大改进单独采用神经网络算法给系统带来的缺陷。
Neural network is one of the most commonly used algorithms in data mining. It has the advantages of a high accuracy, a great anti-yawp capacity, and a low error rate in calculation. However, neural network also has its disadvantage that it has a relatively complicated structure, a longer training period and a deficiency in calculation report interpretation. This paper proposed a new method in which a preprocessing of data using rough sets theory will be followed by data mining using neural rules. With a limited decrease in accuracy, this method provides us with clear, definite and low error rate associate rules, as well as a shorter network training time. The proposed method thus eliminates some of the system limitations caused by of an exclusive/sole application of neural network.