A new associative classifier for text categorization

Text categorization has become one of the key techniques for handling and organizing text data. In practical text classification tasks, the ability to interpret the classification result is as important as the ability to classify exactly. Associative classifiers have many favorable characteristics such as rapid training, good classification accuracy, and excellent interpretation. In this paper, Closed-AC, which is a new associative classifier for text categorization, is proposed. Firstly, rough set is used to dimension reduction. Then, only generic rules composed of closed itemsets are used for classification. Experimental results show benefits of the proposed associative classifier.