I-prune: Item selection for associative classification

Associative classification is characterized by accurate models and high model generation time. Most time is spent in extracting and post-processing a large set of irrelevant rules, which are eventually pruned. We propose I-prune, an item pruning approach that selects uninteresting items by means of an interestingness measure and prunes them as soon as they are detected. Thus, the number of extracted rules is reduced and model generation time decreases correspondingly. A wide set of experiments on real and synthetic datasets has been performed to evaluate I-prune and to select the appropriate interestingness measure. The experimental results show that I-prune allows a significant reduction in model generation time, while increasing (or at worst preserving) model accuracy. Experimental evaluation also points to the chi-square measure as the most effective interestingness measure for item pruning.