An Approach for Arabic Text Categorization Using Association Rule Mining
Qasem A. Al‐Radaideh, Emad M. Al-Shawakfa, Abdullah Saeed Ghareb, Hani Abu‐Salem · International Journal of Computer Processing Of Languages · 2011
Text Categorization (TC) has become one of the major techniques for organizing and managing online information. Several studies proposed the so-called associative classification for databases and few of these studies are proposed to classify text documents into predefined categories based on their contents. In this paper a new approach is proposed for Arabic text categorization. The approach facilitates the discovery of association rules for building a classification model for Arabic text categorization. An apriori based algorithm is employed for association rule mining. To validate the proposed approach, several experiments were applied on a collection of Arabic documents. Three classification methods using association rules were compared in terms of their classification accuracy; the methods are: ordered decision list, weighted rules, and majority voting. The results showed that the majority voting method is the best in most of experiments achieving an accuracy of up to 87%. On the other hand, the weighted rule method was the worst in all experiments. Generally, the results of the experiments showed that association rule mining is a suitable method for building good classification models to categorize Arabic text.