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Associative classification (AC) is a promising data mining approach which builds more accurate classifiers than traditional classification technique such as decision trees and rule induction. By integrating association rules mining with classification, AC has two main phases which are rule generation and classifier building.

In this paper, we investigate one of the well known AC algorithm i.e. MCAR, Naïve Bayesian method (NB) and Support Vector Machine algorithm (SVM) on different Arabic data sets. The bases of our comparison are the most popular text evaluation measures. The Experimental results against different Arabic text categorization data sets reveal that MCAR algorithm outperforms the NB and SVM algorithms with regards to all measures.