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An Information-Theoretic Framework for High-Order Co-Clustering of Heterogeneous Objects.

, , and . SEBD, page 397-404. (2007)

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Combining Different Data Mining Techniques to Improve Data Analysis., , and . FQAS, volume 7 of Advances in Soft Computing, page 455-464. Physica-Verlag Heidelberg New York, A Springer-Verlag Company, (2000)Mining usage scenarios in business processes: Outlier-aware discovery and run-time prediction., , , and . Data Knowl. Eng., 70 (12): 1005-1029 (2011)Pushing More AI Capabilities into Process Mining to Better Deal with Low-Quality Logs., and . Business Process Management Workshops, volume 362 of Lecture Notes in Business Information Processing, page 5-11. Springer, (2019)Process Discovery via Precedence Constraints., , and . ECAI, volume 242 of Frontiers in Artificial Intelligence and Applications, page 366-371. IOS Press, (2012)On the Interpretation of Traces of Low Level Events in Business Process Logs., , , , and . SEBD, volume 2161 of CEUR Workshop Proceedings, CEUR-WS.org, (2018)Argumentation Meets Process Mining: an Architecture for Log Interpretation., , , and . AI³@AI*IA, volume 3086 of CEUR Workshop Proceedings, CEUR-WS.org, (2021)A Data Mining-based Framework for GridWorkflow Management., , , , , , and . QSIC, page 349-356. IEEE Computer Society, (2005)Discovering High-Level Performance Models for Ticket Resolution Processes., , and . OTM Conferences, volume 8185 of Lecture Notes in Computer Science, page 275-282. Springer, (2013)On the Discovery of Explainable and Accurate Behavioral Models for Complex Lowly-structured Business Processes., , and . ICEIS (1), page 206-217. SciTePress, (2015)A compression-based framework for the efficient analysis of business process logs., , , , and . SSDBM, page 6:1-6:12. ACM, (2015)