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Anomaly Detection Toolbox - Beta =========================

Project Website:

http://dsmi-lab-ntust.github.io/AnomalyDetectionToolbox/

This a collection of anomaly detection algorithms. Currently they are split into three categories:

- angle-based methods, Hans-Peter Kriegel, Matthias S hubert, and Arthur Zimek. 2008. Angle-based outlier detection in high-dimensional data. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '08). ACM, New York, NY, USA, 444-452. DOI=10.1145/1401890.1401946 http://doi.acm.org/10.1145/1401890.1401946

- distance-based methods, Keogh, E.; Lin, J.; Fu, A., "HOT SAX: efficiently finding the most unusual time series subsequence," Data Mining, Fifth IEEE International Conference on , vol., no., pp.8 pp.,, 27-30 Nov. 2005

- distribution-based methods. Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng, and Jörg Sander. 2000. LOF: identifying density-based local outliers. SIGMOD Rec. 29, 2 (May 2000), 93-104. DOI=10.1145/335191.335388 http://doi.acm.org/10.1145/335191.335388