A Novel Data Mining Approach for Multi Variant Text Classification

Abstract

Text classification, which aims to assign a document to one or more categories based on its content, is a fundamental task for Web and/or document data mining applications. In natural language processing and information extraction fields Text classification is emerging as an important part, were we can use this approach to discover useful information from large database. These approaches allow individuals to construct classifiers that have relevance for a variety of domains. Existing algorithms such as Svm Light have less GUI support and take more time to perform classification task. In this presented work classification of multi-domain documents is performed by using weka-LibSVM classifier. Here to transform collected training set and test set documents into term-document matrix (TDM), the vector space model is used. In classifier TDM is used to generate predicted results. The results emerged from weka with its GUI support using TDM have quick response time in classifying the documents.