  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.
