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Association rule mining is the most popular technique in data mining. Mining association rules is a prototypical problem as the data are being generated and stored every day in corporate computer database systems. To manage this knowledge, rules have to be pruned and grouped, so that only reasonable numbers of rules have to be inspected and analyzed. In this paper, the authors compare the standard association rule algorithms with the proposed Scaled Association Rules algorithm and AIREP algorithm. All these algorithms are compared according to the various factors like Type of dataset, support counting, rule generation, candidate generation, computational complexity and other factors.
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