Episode mining is a fundamental problem in analyzing a sequence of numerous events. For discovering strong relationships between events in a complex event sequence, episode rule mining is proposed. However, both the episode and episode rules have strict requirements for the order of events. Hence, partially-ordered episode rule mining (POERM) is designed to loosen the constraints on the ordering, i.e., events in the antecedents and consequents of the rule can be unordered, and POERM has been applied to real-life event prediction. In this paper, we consider the utility of POERM, intending to discover more valuable rules. We define the utility of POERs and propose an algorithm called UPER to discover high-utility partially-ordered episode rules. In addition, we adopt a data structure named NoList to store the necessary information, analyze the expansion of POERs in detail, and propose several pruning strategies (namely WEUP, REUCSP, and REEUP) to reduce the number of candidate rules. Finally, we conduct experiments on several datasets to demonstrate the effectivene

翻译：事件挖掘是分析大量事件序列的基础问题。为发现复杂事件序列中事件间的强关联关系，事件规则挖掘方法被提出。然而，事件及事件规则均对事件顺序有严格要求。因此，偏序事件规则挖掘被设计用于放宽对顺序的约束，即规则前件与后件中的事件可为无序状态，该方法已应用于现实事件预测。本文考虑偏序事件规则挖掘的效用问题，旨在发现更具价值的规则。我们定义了偏序事件规则的效用，并提出名为UPER的算法以挖掘高效用偏序事件规则。此外，我们采用名为NoList的数据结构存储必要信息，详细分析偏序事件规则的扩展过程，并提出多种剪枝策略（即WEUP、REUCSP和REEUP）以减少候选规则数量。最后，我们在多个数据集上进行实验以验证其有效性。