Data Mining Techniques for Text Mining

Abstract

Text mining is a variation on a field called data mining that tries to find interesting patterns from large databases. Classification is a major data mining task. It is often referred to as supervised learning because the classes are determined before examining the data. 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. The aim of this paper is the classification algorithms are applied to classify the intrusion detection data sets like Signature Verification.