The Utility-Linked (UL)-list structure of <i>S</i>₄.

High utility sequential pattern (HUSP) mining aims to mine actionable patterns with high utilities, widely applied in real-world learning scenarios such as market basket analysis, scenic route planning and click-stream analysis. The existing HUSP mining algorithms mainly attempt to improve computation efficiency while maintaining the algorithm stability in the setting of large-scale data. Although these methods have made some progress, they ignore the relationship between additional items and underlying sequences, which directly leads to the generation of redundant sequential patterns sharing the same underlying sequence. Hence, the mined patterns’ actionability is limited, which significantly compromises the performance of patterns in real-world applications. To address this problem, we present a new method named Combined Utility-Association Sequential Pattern Mining (CUASPM) by incorporating item/sequence relations, which can effectively remove redundant patterns and extract high discriminative and strongly associated sequential pattern combinations with high utilities. Specifically, we introduce the concept of actionable combined mining into HUSP mining for the first time and develop a novel tree structure to select discriminative high utility sequential patterns (HUSPs) for downstream tasks. Furthermore, two efficient strategies (i.e., global and local strategies) are presented to facilitate mining HUSPs while guaranteeing utility growth and high levels of association. Last, two parameters are introduced to evaluate the interestingness of patterns to choose the most useful actionable combined HUSPs (ACHUSPs). Extensive experimental results demonstrate that the proposed CUASPM outperforms the baselines in terms of execution time, memory usage, mining high discriminative and strongly associated HUSPs.高效用序列模式（High Utility Sequential Pattern, HUSP）挖掘旨在挖掘具备高效用的可操作模式，已广泛应用于各类现实学习场景，例如购物篮分析、景区路线规划与点击流分析等。现有HUSP挖掘算法主要致力于在大规模数据场景下提升计算效率的同时保障算法稳定性。尽管此类方法已取得一定进展，但它们忽略了附加项与底层序列之间的关联关系，这直接导致生成了共享同一底层序列的冗余序列模式。因此，所挖掘模式的可操作性受限，这极大削弱了模式在现实应用中的表现效果。为解决这一问题，本文提出一种融合项/序列关联关系的新型方法——联合效用-关联序列模式挖掘（Combined Utility-Association Sequential Pattern Mining, CUASPM），该方法可有效剔除冗余模式，并提取具备高效用的高判别性强关联序列模式组合。具体而言，本文首次将可操作联合挖掘的概念引入HUSP挖掘领域，并构建了一种全新的树结构，用于为下游任务筛选具备判别性的高效用序列模式（High Utility Sequential Patterns, HUSPs）。此外，本文提出两种高效策略（即全局策略与局部策略），在保障效用增长与高关联度的同时，助力HUSP挖掘任务的开展。最后，本文引入两个参数用于评估模式的有趣度，以此筛选出最具实用价值的可操作联合高效用序列模式（Actionable Combined High Utility Sequential Patterns, ACHUSPs）。大量实验结果表明，所提出的CUASPM在执行时长、内存占用以及高判别性强关联HUSP的挖掘效果方面均优于基线方法。