Selecting Optimal Trace Clustering Pipelines with Meta-learning

Gabriel Marques Tavares, Sylvio Barbon Junior, Ernesto Damiani, Paolo Ceravolo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations


Trace clustering has been extensively used to discover aspects of the data from event logs. Process Mining techniques guide the identification of sub-logs by grouping traces with similar behaviors, producing more understandable models and improving conformance indicators. Nevertheless, little attention has been posed to the relationship among event log properties, the pipeline of encoding and clustering algorithms, and the quality of the obtained outcome. The present study contributes to the understanding of the aforementioned relationships and provides an automatic selection of a proper combination of algorithms for clustering a given event log. We propose a Meta-Learning framework to recommend the most suitable pipeline for trace clustering, 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 environment.

Original languageBritish English
Title of host publicationIntelligent Systems - 11th Brazilian Conference, BRACIS 2022, Proceedings
EditorsJoão Carlos Xavier-Junior, Ricardo Araújo Rios
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages15
ISBN (Print)9783031216855
StatePublished - 2022
Event11th Brazilian Conference on Intelligent Systems, BRACIS 2022 - Campinas, Brazil
Duration: 28 Nov 20221 Dec 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13653 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference11th Brazilian Conference on Intelligent Systems, BRACIS 2022


  • Meta-learning
  • Pipeline design
  • Process mining
  • Recommendation
  • Trace clustering


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