The era characterized by an exponential increase in data has led to the widespread adoption of data intelligence as a crucial task. Within the field of data mining, frequent episode mining has emerged as an effective tool for extracting valuable and essential information from event sequences. Various algorithms have been developed to discover frequent episodes and subsequently derive episode rules using the frequency function and anti-monotonicity principles. However, currently, there is a lack of algorithms specifically designed for mining episode rules that encompass user-specified query episodes. To address this challenge and enable the mining of target episode rules, we introduce the definition of targeted precise-positioning episode rules and formulate the problem of targeted mining precise-positioning episode rules. Most importantly, we develop an algorithm called Targeted Mining Precision Episode Rules (TaMIPER) to address the problem and optimize it using four proposed strategies, leading to significant reductions in both time and space resource requirements. As a result, TaMIPER offers high accuracy and efficiency in mining episode rules of user interest and holds promising potential for prediction tasks in various domains, such as weather observation, network intrusion, and e-commerce. Experimental results on six real datasets demonstrate the exceptional performance of TaMIPER.

翻译：数据呈指数级增长的时代使得数据智能成为一项至关重要的任务。在数据挖掘领域，频繁事件序列挖掘已成为从事件序列中提取有价值关键信息的有效工具。目前已开发出多种算法来发现频繁事件序列，并利用频率函数和反单调性原理进一步推导出关联事件规则。然而，当前尚缺乏专门针对包含用户指定查询事件的关联事件规则进行挖掘的算法。为应对这一挑战并实现目标关联事件规则的挖掘，我们提出了面向精准定位的关联事件规则的定义，并构建了面向精准定位的关联事件规则定向挖掘问题。最重要的是，我们开发了一种名为TaMIPER的算法来解决该问题，并通过四种优化策略显著降低了时间和空间资源需求。因此，TaMIPER在挖掘用户感兴趣的关联事件规则方面具有高精度和高效率，并在天气观测、网络入侵检测和电子商务等多个领域的预测任务中展现出广阔的应用前景。在六个真实数据集上的实验结果验证了TaMIPER的卓越性能。