基于RBF神经网络的数据挖掘方法探究

Research on Data Mining Method Based on RBF Neural Network

在互联网技术快速发展和数据库技术广泛应用的同时,人类通过信息技术搜集数据的能力越来越强,而如何从大量数据中挖掘有价值的知识或信息也变得尤为迫切。为了解决上述问题,数据挖掘技术应运而生。研究发现,数据挖掘所需处理的数据多为非线性的、杂乱和存在噪声的数据,神经网络正是凭借其高度容错性、分布存储、并行处理、自适应性和鲁棒性等特征而被广泛用来处理一些数据挖掘的问题。据此,在本案,笔者首先介绍数据挖掘与RBF神经网络的相关理论知识;然后再重点讨论基于RBF神经网络的数据挖掘方法,以供同行参考。

The rapid development of Internet technology and database technology is widely used at the same time, human through information technology to collect data is more and more strong, and how to from a lot of data mining valuable information and knowledge has become particularly urgent. In order to solve the above problems, data mining technology arises at the historic moment. It is found that the data mining the data for the nonlinear, messy and the presence of noise data, neural network is by virtue of the degree of fault tolerance, distributed storage, parallel processing, adaptive and robust feature is widely used to deal with some of the data mining problems. Accordingly, in this case, the author first introduces the data mining and RBF neural network of the relevant theoretical knowledge, and then focus on the RBF neural network based on the data mining method for peer reference.