目标挖掘精确定位情节规则
Targeted Mining Precise-positioning Episode Rules
AI总结：
本文提出目标精确定位情节规则定义及TaMIPER算法，通过四种优化策略高效挖掘用户指定查询情节的规则，在多个领域具有预测应用潜力。
摘要：
以数据指数级增长为特征的时代促使数据智能作为一项关键任务得到广泛应用。在数据挖掘领域，频繁情节挖掘已成为从事件序列中提取有价值且重要信息的有效工具。已有多种算法被开发用于发现频繁情节，并随后利用频率函数和反单调性原理推导情节规则。然而，目前缺乏专门设计用于挖掘包含用户指定查询情节的情节规则的算法。为应对这一挑战并实现目标情节规则的挖掘，我们引入了目标精确定位情节规则的定义，并形式化了目标挖掘精确定位情节规则问题。最重要的是，我们开发了一种名为Targeted Mining Precision Episode Rules（TaMIPER）的算法来解决该问题，并利用四种提出的策略对其进行优化，从而显著降低时间和空间资源需求。因此，TaMIPER在挖掘用户感兴趣的情节规则方面具有高准确性和高效率，并在天气观测、网络入侵和电子商务等多个领域的预测任务中具有广阔潜力。在六个真实数据集上的实验结果表明了TaMIPER的卓越性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
