TY - JFULL AU - M. Govindarajan and R. M.Chandrasekaran PY - 2009/3/ TI - Performance Optimization of Data Mining Application Using Radial Basis Function Classifier T2 - International Journal of Computer and Information Engineering SP - 336 EP - 342 VL - 3 SN - 1307-6892 UR - https://publications.waset.org/pdf/9096 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 26, 2009 N2 - Text data mining is a process of exploratory data analysis. Classification maps data into predefined groups or classes. It is often referred to as supervised learning because the classes are determined before examining the data. This paper describes proposed radial basis function Classifier that performs comparative crossvalidation for existing radial basis function Classifier. The feasibility and the benefits of the proposed approach are demonstrated by means of data mining problem: direct Marketing. Direct marketing has become an important application field of data mining. Comparative Cross-validation involves estimation of accuracy by either stratified k-fold cross-validation or equivalent repeated random subsampling. While the proposed method may have high bias; its performance (accuracy estimation in our case) may be poor due to high variance. Thus the accuracy with proposed radial basis function Classifier was less than with the existing radial basis function Classifier. However there is smaller the improvement in runtime and larger improvement in precision and recall. In the proposed method Classification accuracy and prediction accuracy are determined where the prediction accuracy is comparatively high. ER -