Incorporating utility into targeted pattern mining can address the practical limitations of traditional frequency-based approaches. However, utility-based methods often suffer from generating a large number of long and complicated sequences. To improve pattern relevance and interpretability, average utility provides a more balanced metric by considering both utility and sequence length. Moreover, incorporating user-defined query targets into the mining process enhances usability and interactivity by retaining only patterns containing user-specified goals. To address challenges related to mining efficiency in large-scale, long-sequence datasets, this study introduces average utility into targeted sequential pattern mining. A novel algorithm, TAUSQ-PG, is designed to find targeted high average utility sequential patterns. It incorporates efficient filtering and pruning strategies, tighter upper bound models, as well as novel specialized evaluation metrics and query flags tailored to this task. Extensive comparative experiments on different datasets demonstrate that TAUSQ-PG effectively controls the candidate set size, thereby reducing redundant sequence generation and significantly improving runtime and memory efficiency.

翻译：将效用纳入目标模式挖掘可以解决传统基于频率方法的实际局限性。然而，基于效用的方法通常会产生大量冗长且复杂的序列。为提高模式相关性和可解释性，平均效用通过同时考虑效用和序列长度，提供了一种更为平衡的度量标准。此外，将用户定义的查询目标纳入挖掘过程，通过仅保留包含用户指定目标的模式，增强了可用性和交互性。为应对大规模、长序列数据集中挖掘效率相关的挑战，本研究将平均效用引入面向目标的序列模式挖掘。设计了一种新颖算法 TAUSQ-PG，用于发现面向目标的高平均效用序列模式。该算法融合了高效的过滤与剪枝策略、更紧凑的上界模型，以及为此任务量身定制的新型专用评估指标和查询标志。在不同数据集上进行的大量对比实验表明，TAUSQ-PG 能有效控制候选集规模，从而减少冗余序列生成，并显著提升运行时间和内存效率。