Research On Data Mining Based On RBF Neural Networks

With the wide application of databases and sharp development of Internet, the capacity of utilizing information technology to manufacture and collect data has improved greatly. It is an urgent problem to mine useful information or knowledge from large databases or data warehouses. Therefore, data mining technology is developed rapidly to meet the need. But data mining (DM) often faces so much data which is noisy, disorder and nonlinear. Fortunately, artificial neural network (ANN) is suitable to solve the before-mentioned problems of DM because ANN has such merits as good robustness, adaptability, parallel-disposal, distributing-memory and high tolerating-error.This paper simply expounds the basic theory of DM and ANN. Based on the analysis of all kinds of data mining technology, gives a detailed discussion about the application of ANN method used in DM, and especially lays stress on the classification of DM which is based on radial basis function (RBF) neural networks. An incremental learning algorithm (ILA) is deduced from the gradient descend algorithm. ILA can adjust parameters of RBF networks adaptively driven by minimizing the error cost. And then applied it to resolve the IRIS problem, the experiment results show the algorithm has excellent performance.Based on the thorough research of the learning algorithm of RBF neural networks, a two-stage (TS) learning strategy is used to accelerate the convergence rate of traditional gradient descent algorithm; And a new method is proposed to design the hidden layer of RBF network by dynamic and static style; In order to improve the output precision of RBF networks, a creative algorithm named error-correcting (EC) is proposed for the first time in this paper. Afterward, these improving algorithms are tested by two UCI databases to evaluate their capability. Experiment results show that their performances are improved obviously.Additionally, basing on our research of data mining and neural networks technology, a data mining system for classification and prediction, which integrate all creative algorithm proposed in this paper, is developed primarily as an experiment platform.To some degree, this article has some theory significance and practical value. Especially some creative algorithm presented in this paper can provide helpful reference to related researcher.