Optimized Association Rule Based Text Categorization with (oartc ) Algorithm

Text based classification is the process to classify the documents into pre-defined categories based on their content. The task of data mining is to classify the documents automatically into predefined classes based on their content. Existing supervised learning algorithms are to classify text need sufficient documents to learn accurately. This paper presents a new algorithm for text classification using data mining that requires fewer documents for training. Instead of using words, word relation, i.e the association rules from these words, is used to derive feature set from pre-classified text documents. News Group data set consists of twenty thousand message is one which is widely used. Calculate the distance to classify the test samples. Before classification initially the reduced feature set is received from TF/ IDF method which was discussed already in the our earlier work. Finally, the association rules are formed by Aprior Algorithm and term sets are also formed.