VolTime: Unsupervised Anomaly Detection on Users' Online Activity Volume. Chino, D. Y. T., Costa, A. F., Traina, A. J. M., & Faloutsos, C. In Chawla, N. V. & Wang, W., editors, Proceedings of the 2017 SIAM International Conference on Data Mining, Houston, Texas, USA, April 27-29, 2017, pages 108–116, 2017. SIAM.
VolTime: Unsupervised Anomaly Detection on Users' Online Activity Volume [link]Paper  doi  bibtex   
@inproceedings{DBLP:conf/sdm/ChinoCTF17,
  author       = {Daniel Y. T. Chino and
                  Alceu Ferraz Costa and
                  Agma J. M. Traina and
                  Christos Faloutsos},
  editor       = {Nitesh V. Chawla and
                  Wei Wang},
  title        = {VolTime: Unsupervised Anomaly Detection on Users' Online Activity
                  Volume},
  booktitle    = {Proceedings of the 2017 {SIAM} International Conference on Data Mining,
                  Houston, Texas, USA, April 27-29, 2017},
  pages        = {108--116},
  publisher    = {{SIAM}},
  year         = {2017},
  url          = {https://doi.org/10.1137/1.9781611974973.13},
  doi          = {10.1137/1.9781611974973.13},
  timestamp    = {Fri, 04 Oct 2019 16:19:51 +0200},
  biburl       = {https://dblp.org/rec/conf/sdm/ChinoCTF17.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

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