Classifier Based Text Mining Approaches for Data Mining Applications

Abstract

Text mining is a process of extracting the information from an unstructured text. This research work deals with several classifiers including k-Nearest Neighbor (k-NN), Radial Basis Function (RBF), Multilayer Perceptron (MLP), and Support Vector Machine (SVM) which are used as trained classifiers for performing classification of data into relevant and non-relevant data. This study intends to compare the efficiency of the various existing classification algorithms with the proposed classification algorithms on the basis of runtime, error rate and accuracy.