High utility sequential pattern mining (HUSPM) aims to mine all patterns that yield a high utility (profit) in a sequence dataset. HUSPM is useful for several applications such as market basket analysis, marketing, and website clickstream analysis. In these applications, users may also consider high utility patterns frequently appearing in the dataset to obtain more fruitful information. However, this task is high computation since algorithms may generate a combinatorial explosive number of candidates that may be redundant or of low importance. To reduce complexity and obtain a compact set of frequent high utility sequential patterns (FHUSPs), this paper proposes an algorithm named CHUSP for mining closed frequent high utility sequential patterns (CHUSPs). Such patterns keep a concise representation while preserving the same expressive power of the complete set of FHUSPs. The proposed algorithm relies on a CHUS data structure to maintain information during mining. It uses three pruning strategies to eliminate early low-utility and non-frequent patterns, thereby reducing the search space. An extensive experimental evaluation was performed on six real-life datasets to evaluate the performance of CHUSP in terms of execution time, memory usage, and the number of generated patterns. Experimental results show that CHUSP can efficiently discover the compact set of CHUSPs under different user-defined thresholds.

翻译：高效用序列模式挖掘（HUSPM）旨在从序列数据集中找出所有产生高效用（利润）的模式。HUSPM在购物篮分析、市场营销和网站点击流分析等多个应用场景中具有重要价值。在这些应用中，用户可能还需要考虑数据集中频繁出现的高效用模式，以获取更丰富的信息。然而，该任务计算量巨大，因为算法可能生成组合爆炸式的候选模式，其中存在冗余或重要性较低的模式。为降低复杂度并获取一组紧凑的频繁高效用序列模式（FHUSP），本文提出一种名为CHUSP的算法，用于挖掘闭合频繁高效用序列模式（CHUSP）。这类模式在保持与完整FHUSP集合同等表达能力的同时，实现了简洁的表示。所提算法基于CHUS数据结构以在挖掘过程中维护信息，并采用三种剪枝策略预先剔除低效用和非频繁模式，从而缩小搜索空间。在六个真实数据集上进行了全面的实验评估，从执行时间、内存消耗和生成模式数量三个维度评价CHUSP的性能。实验结果表明，CHUSP能够在不同用户定义阈值下高效地发现紧凑的CHUSP集合。