一种有效的周期高效用序列模式增量挖掘算法

Effective incremental mining algorithm forperiodic high-utility sequential patterns

周期高效用序列模式挖掘(PHUSPM)因其能够发现时间序列中更具实际价值的规律性模式而备受关注,但现有的PHUSPM算法难以有效地处理数据集的增量更新,且未考虑大规模数据下算法的向下闭包性和复杂性。针对该问题,提出了IncPUS-Miner算法,有效地实现了周期高效用序列模式(PHUSPs)的增量挖掘。IncPUS-Miner引入了一种名为pu-tree的新型数据结构,每个树节点对应一个更新效用列表(UUL)用于存储相应序列的辅助信息,当有增量数据加入时,该结构使得项目信息能够灵活更新,从而增强了算法的动态适应性和可扩展性。此外,还提出了两种新的序列效用上界PUB和EUB,以及两种相应的剪枝策略,有效地减少了计算负担。实验结果表明,在真实数据集上,IncPUS-Miner算法可以有效地增量挖掘PHUSPs,与其他算法相比,在运行效率和内存消耗上展现出了优越的性能。

Periodic high utility sequential pattern mining(PHUSPM)has attracted much attention because it can find more practical regular patterns in time series.However,existing PHUSPM algorithms struggle to effectively handle incremental updates and overlook the downward closure property and complexity of the algorithm in large-scale data.To solve this problem,this paper proposed an IncPUS-Miner algorithm,which effectively realized the incremental mining of periodic high-utility sequential patterns(PHUSPs).IncPUS-Miner introduced a novel data structure called pu-tree.Each tree node corresponded to an updated utility list(UUL)to store the auxiliary information of the corresponding sequence.When incremental data was added,this structure allowed flexible updates to project information,thereby enhancing the dynamic adaptability and scalability of the algorithm.In addition,this paper proposed two new upper bounds of sequence utility,PUB and EUB,and two corresponding pruning stra-tegies,which effectively reduced the computational burden.The experimental results show that the IncPUS-Miner algorithm effectively realizes the incremental mining of PHUSPs on real data,and shows superior performance compared with other algorithms.