Abstract An aggregate constraint with items of varying values (ACV) is introduced. The ACV constraint is used to express users requirement on the aggregate feature of target patterns. An algorithm for mining frequent sequential patterns with the ACV constraint is proposed. It exploits the computational properties of ACV to effectively prune the search space. Experimental results on both the synthetic sequential data generated by IBM data generator and a real world data set show that the proposed algorithm utilizes ACV constraints to prune the useless candidate sequential patterns, and it reduces the redundant search space to improve the mining efficiency.
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