HHUIM:一种新的启发式高效用项集挖掘方法OACSTPCD

In response to the problem of potentially losing a large number of itemsets during the mining process of heuristic-based high utility itemset mining algorithms,this paper proposed a new heuristic-based high utility itemset mining algorithm,called HHUIM.HHUIM utilized the Harris hawk optimization algorithm for population update,effectively reducing the loss of itemsets.This paper also introduced and designed a hawk replacement strategy to solve the problem of a large search space by decreasing the number of hawks with fitness values below the minimum utility threshold.Furthermore,this paper proposed a storage backtracking strategy to prevent premature convergence to local optima.Extensive experiments demonstrate that the proposed algorithm outperforms the state-of-the-art heuristic-based high utility itemset mining algorithms.