Frequent pattern mining is one of the active research themes in data mining. It plays an important role in all data mining tasks such as clustering, classification, prediction, and association analysis. Identifying all frequent patterns is the most time consuming process due to a massive number of patterns generated. A reasonable solution is identifying efficient method to finding frequent patterns without candidate generation. In this paper, we present An Evolutionary algorithm for mining association rules using Boolean approach for mining association rules in large databases of sales transactions. Traditional association rule algorithms adopt an iterative method to discovery, which requires very large calculations and a complicated transaction process. Because of this, a new association rule algorithm is proposed in this paper. This new algorithm scanning the database once and avoiding generating candidate itemsets in computing frequent itemset and adopts a Boolean vector “relational calculus” method to discovering frequent itemsets. Experimental results show that this algorithm can quickly discover frequent itemsets and effectively mine potential association rules.
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[v1] 2012-08-18 21:32:21
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