A novel feature selection method for text classification using association rules and clustering

Readability and accuracy are two important features of any good classifier. For reasons such as acceptable accuracy, rapid training and high interpretability, associative classifiers have recently been used in many categorization tasks. Although features could be very useful in text classification, both training time and the number of produced rules will increase significantly owing to the high dimensionality of text documents. In this paper an association classification algorithm for text classification is proposed that includes a feature selection phase to select important features and a clustering phase based on class labels to tackle this shortcoming. The experimental results from applying the proposed algorithm in comparison with the results of selected well-known classification algorithms show that our approach outperforms others both in efficiency and in performance.