document: Real world performance of association rule algorithms This study compares five well-known association rule algorithms using three real-world datasets and an artificial dataset . The experimental results confirm the performance improvements previously claimed by the authors on the artificial data , but some of these gains do not carry over to the real datasets , indicating overfitting of the algorithms to the IBM artificial dataset . More importantly , we found that the choice of algorithm only matters at support levels that generate more rules than would be useful in practice . For support levels that generate less than 1,000,000 rules , which is much more than humans can handle and is sufficient for prediction purposes where data is loaded into RAM , Apriori finishes processing in less than 10 minutes . On our datasets , we observed super-exponential growth in the number of rules . On one of our datasets , a 0.02 % change in the support increased the number of rules from less than a million to over a billion , implying that outside a very narrow range of support values , the choice of algorithm is irrelevant .
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