Concept drift detection and localization in process mining: an integrated and efficient approach enabled by trace clustering (2021)
- Authors:
- USP affiliated authors: PERES, SARAJANE MARQUES - EACH ; FANTINATO, MARCELO - EACH ; SOUSA, RAFAEL GASPAR DE - EACH
- Unidade: EACH
- DOI: 10.1145/3412841.3441918
- Subjects: MINERAÇÃO DE DADOS; NEGÓCIOS
- Language: Inglês
- Imprenta:
- Source:
- Título: Proceedings
- Conference titles: Annual ACM Symposium on Applied Computing
- Status:
- Nenhuma versão em acesso aberto identificada
-
ABNT
SOUSA, Rafael Gaspar de et al. Concept drift detection and localization in process mining: an integrated and efficient approach enabled by trace clustering. 2021, Anais.. New York: ACM, 2021. Disponível em: https://doi.org/10.1145/3412841.3441918. Acesso em: 09 abr. 2026. -
APA
Sousa, R. G. de, Peres, S. M., Fantinato, M., & Reijers, H. A. (2021). Concept drift detection and localization in process mining: an integrated and efficient approach enabled by trace clustering. In Proceedings. New York: ACM. doi:10.1145/3412841.3441918 -
NLM
Sousa RG de, Peres SM, Fantinato M, Reijers HA. Concept drift detection and localization in process mining: an integrated and efficient approach enabled by trace clustering [Internet]. Proceedings. 2021 ;[citado 2026 abr. 09 ] Available from: https://doi.org/10.1145/3412841.3441918 -
Vancouver
Sousa RG de, Peres SM, Fantinato M, Reijers HA. Concept drift detection and localization in process mining: an integrated and efficient approach enabled by trace clustering [Internet]. Proceedings. 2021 ;[citado 2026 abr. 09 ] Available from: https://doi.org/10.1145/3412841.3441918 - X-Processes: process model discovery with the best balance among fitness, precision, simplicity, and generalization through a genetic algorithm
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