用AutoML选择最优轨迹聚类流水线
Selecting Optimal Trace Clustering Pipelines with AutoML
摘要：
跟踪聚类已广泛用于预处理事件日志。通过对相似的行为进行分组，这些技术可以指导子日志的识别，从而生成更易于理解的模型和一致性分析。然而，很少有人关注事件日志属性和聚类质量之间的关系。在这项工作中，我们提出了一个自动机器学习（AutoML）框架，建议最合适的管道跟踪聚类给定的事件日志，其中包括编码方法，聚类算法，及其超参数。我们的实验使用了一千个事件日志，四种编码技术和三种聚类方法。结果表明，我们的框架揭示了跟踪聚类问题，可以帮助用户选择最好的管道考虑他们的情况。
英文摘要：
Trace clustering has been extensively used to preprocess event logs. By grouping similar behavior, these techniques guide the identification of sub-logs, producing more understandable models and conformance analytics. Nevertheless, little attention has been posed to the relationship between event log properties and clustering quality. In this work, we propose an Automatic Machine Learning (AutoML) framework to recommend the most suitable pipeline for trace clustering given an event log, which encompasses the encoding method, clustering algorithm, and its hyperparameters. Our experiments were conducted using a thousand event logs, four encoding techniques, and three clustering methods. Results indicate that our framework sheds light on the trace clustering problem and can assist users in choosing the best pipeline considering their scenario.
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。
