面向目标的顺序规则
Towards Target Sequential Rules
摘要：
在许多实际应用中，顺序规则挖掘（SRM）可以为各种服务提供预测和推荐功能。发现属于高频、高置信度序列规则的所有有价值的规则是模式挖掘的一项重要技术。虽然已有多种SRM算法被提出来解决各种实际问题，但目前还没有针对目标序列规则的研究。有针对性的顺序规则挖掘旨在挖掘用户关注的有趣的顺序规则，从而避免产生其他无效和不必要的规则。该方法可以进一步提高用户分析规则的效率，减少数据资源的消耗。本文给出了目标序列规则的相关定义，阐述了目标序列规则的挖掘问题。此外，我们提出了一个有效的算法，称为有针对性的顺序规则挖掘（TaSRM）。为了提高TaSRM的效率，提出了几种剪枝策略和一种优化方法.最后，在不同的基准测试上进行了大量的实验，并从运行时间、内存消耗、可扩展性以及不同查询规则的查询案例等方面对实验结果进行了分析。结果表明，新算法TaSRM及其变体可以实现更好的实验性能相比，现有的基线算法。
英文摘要：
In many real-world applications, sequential rule mining (SRM) can provide prediction and recommendation functions for a variety of services. It is an important technique of pattern mining to discover all valuable rules that belong to high-frequency and high-confidence sequential rules. Although several algorithms of SRM are proposed to solve various practical problems, there are no studies on target sequential rules. Targeted sequential rule mining aims at mining the interesting sequential rules that users focus on, thus avoiding the generation of other invalid and unnecessary rules. This approach can further improve the efficiency of users in analyzing rules and reduce the consumption of data resources. In this paper, we provide the relevant definitions of target sequential rule and formulate the problem of targeted sequential rule mining. Furthermore, we propose an efficient algorithm, called targeted sequential rule mining (TaSRM). Several pruning strategies and an optimization are introduced to improve the efficiency of TaSRM. Finally, a large number of experiments are conducted on different benchmarks, and we analyze the results in terms of their running time, memory consumption, and scalability, as well as query cases with different query rules. It is shown that the novel algorithm TaSRM and its variants can achieve better experimental performance compared to the existing baseline algorithm.
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。
