An efficient approach for generating frequent patterns without candidate generation
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
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It is well known that the classical association rules mining algorithm suffers the problems such as the low efficiency to generate the frequent itemsets. It needs to scan the database multiple times and often generate redundant candidate itemsets. This paper proposes a vector operation based association rule mining algorithm to solve the problem, which needs only to scan the transaction database one time to generate a Boolean matrix, and the frequent itemsets can be found out via the vector computation on the matrix. The experimental results on Coronary Heart Disease data set, including comparisons with the common Apriori approach, illustrate the effectiveness of the proposed algorithm.