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Finding a unique Association Rule Mining algorithm based on data characteristics

This research compares the performance of three popular association rule mining algorithms, namely apriori, predictive apriori and tertius based on data characteristics. The accuracy measure is used as the performance measure for ranking the algorithms. A wide variety of association rule mining algorithms can create a time consuming problem for choosing the most suitable one for performing the rule mining task. A meta-learning technique is implemented for a unique selection from a set of association rule mining algorithms. On the basis of experimental results of 15 UCI data sets, this research discovers statistical information based rules to choose a more effective algorithm.