Associative classification algorithms which are based on association rules have performed well compared with other classification approaches. However a fundamental limitation with these classification algorithms is that the search space of candidate rules is very large and the processes of rule discovery and rule selection are conducted separately. This paper proposes an algorithm based on immune optimization mechanism for optimizing associative classification rules. In the proposed algorithm the rule search process and the rule selection process are integrated in a more reasonable way in the optimization process of associative rules, thus it has the capability of dealing with complex search space of association rules while still ensuring that the resultant set of association rules is appropriate for associative classification. The performance evaluation results have shown that the proposed algorithm has achieved good runtime and accuracy performance for categorical and text datasets in comparison with conventional associative classification algorithms.
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