Finding frequent items: novel method for improving Apriori algorithm

Abstract

In this paper, we use an intelligent method for improving the Apriori algorithm in order to extract frequent itemsets. PAA (the proposed Apriori algorithm) pursues two goals: first, it is not necessary to take only one data item at each step – in fact, all possible combinations of items can be generated at each step, and second, we can scan only some transactions instead of scanning all of the transactions to obtain a frequent itemset. For performance evaluation, we conducted three experiments with the traditional Apriori, BitTableFI, TDM-MFI, and MDC-Apriori algorithms. The results exhibited that the algorithm execution time was significantly reduced due to the significant reduction in the number of transaction scans to obtain the itemset. As in the first experiment, the time that was spent to generate frequent items underwent a reduction of 52% as compared to the algorithm in the first experiment. In the second experiment, the amount of time that was spent was equal to 65%, while in the third experiment, it was equal to 46%.