References

AF07

Fabrizio Angiulli and Fabio Fassetti. Detecting distance-based outliers in streams of data. In Proceedings of the 16th ACM Conference on Information and Knowledge Management, CIKM'07, 811–820. New York, NY, USA, 2007. ACM.

BKNS00

Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng, and Jörg Sander. LOF: Identifying density-based local outliers. In Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, 93–104. 2000.

GD12

Markus Goldstein and Andreas Dengel. Histogram-based outlier score (HBOS): A fast unsupervised anomaly detection algorithm. In KI-2012: Poster and Demo Track. 35th German Conference on Artificial Intelligence (KI-2012), 59–63. 2012.

GMRS16

Sudipto Guha, Nina Mishra, Gourav Roy, and Okke Schrijvers. Robust random cut forest based anomaly detection on streams. In Proceedings of The 33rd International Conference on Machine Learning, volume 48 of Proceedings of Machine Learning Research, 2712–2721. New York, New York, USA, 20–22 Jun 2016. PMLR.

HIZ20

Alexander Hartl, Félix Iglesias, and Tanja Zseby. SDOstream: low-density models for streaming outlier detection. In ESANN 2020 proceedings, 661–666. 2020.

KGP+11

Maria Kontaki, Anastasios Gounaris, Apostolos N. Papadopoulos, Kostas Tsichlas, and Yannis Manolopoulos. Continuous monitoring of distance-based outliers over data streams. In IEEE 27th International Conference on Data Engineering, volume, 135–146. April 2011.

MLA18

Emaad A. Manzoor, Hemank Lamba, and Leman Akoglu. xStream: Outlier detection in feature-evolving data streams. In 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2018.

Pevny16

Tomáš Pevný. Loda: lightweight on-line detector of anomalies. Machine Learning, 102(2):275–304, February 2016.

SA16

Saket Sathe and Charu C. Aggarwal. Subspace outlier detection in linear time with randomized hashing. In 2016 IEEE 16th International Conference on Data Mining (ICDM), 459–468. 2016.

TTL11

Swee Chuan Tan, Kai Ming Ting, and Tony Fei Liu. Fast anomaly detection for streaming data. In Twenty-Second International Joint Conference on Artificial Intelligence. 2011.

YRW09

Di Yang, Elke A. Rundensteiner, and Matthew O. Ward. Neighbor-based pattern detection for windows over streaming data. In Proceedings of the 12th International Conference on Extending Database Technology, EDBT'09, 529–540. New York, NY, USA, 2009. ACM.