Optimizing data mining tasks using rbf neural networks

This paper deals with the application of a well-known neural network technique, radial basis neural network for optimizing accuracy in data mining. In many data mining applications that address classification problems, feature and model selection are considered as key tasks. That is, appropriate input features of the classifier must be selected from a given (and often large) set of possible features and structure parameters of the classifier must be adapted with respect to these features and a given data set. This article elaborates on the role of neural networks in data mining, especially classification, and presents various ways of using them in this area. In order to do this the radial basis neural networks are reviewed, and an overview of the classification process and the training of neural networks are given.