VolTime: unsupervised anomaly detection on users' online activity volume (2017)
Chino, Daniel Y. T; Costa, Alceu F; Traina, Agma Juci Machado 
; Faloutsos, Christos
Source: Proceedings. Conference titles: SIAM International Conference on Data Mining - SDM. Unidade: ICMC
Subjects: BANCO DE DADOS, RECONHECIMENTO DE PADRÕES
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ABNT
CHINO, Daniel Y. T et al. VolTime: unsupervised anomaly detection on users' online activity volume. 2017, Anais.. Philadelphia: SIAM, 2017. Disponível em: https://doi.org/10.1137/1.9781611974973.13. Acesso em: 01 out. 2024.APA
Chino, D. Y. T., Costa, A. F., Traina, A. J. M., & Faloutsos, C. (2017). VolTime: unsupervised anomaly detection on users' online activity volume. In Proceedings. Philadelphia: SIAM. doi:10.1137/1.9781611974973.13NLM
Chino DYT, Costa AF, Traina AJM, Faloutsos C. VolTime: unsupervised anomaly detection on users' online activity volume [Internet]. Proceedings. 2017 ;[citado 2024 out. 01 ] Available from: https://doi.org/10.1137/1.9781611974973.13Vancouver
Chino DYT, Costa AF, Traina AJM, Faloutsos C. VolTime: unsupervised anomaly detection on users' online activity volume [Internet]. Proceedings. 2017 ;[citado 2024 out. 01 ] Available from: https://doi.org/10.1137/1.9781611974973.13
