PyNomaly: Anomaly detection using Local Outlier Probabilities (LoOP).

Python Submitted 08 May 2018Published 27 October 2018
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Editor: @Kevin-Mattheus-Moerman (all papers)
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Authors

Valentino Constantinou (0000-0002-5279-4143)

Citation

Constantinou, (2018). PyNomaly: Anomaly detection using Local Outlier Probabilities (LoOP).. Journal of Open Source Software, 3(30), 845, https://doi.org/10.21105/joss.00845

@article{Constantinou2018, doi = {10.21105/joss.00845}, url = {https://doi.org/10.21105/joss.00845}, year = {2018}, publisher = {The Open Journal}, volume = {3}, number = {30}, pages = {845}, author = {Valentino Constantinou}, title = {PyNomaly: Anomaly detection using Local Outlier Probabilities (LoOP).}, journal = {Journal of Open Source Software} }
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outlier detection anomaly detection probability nearest neighbors unsupervised learning machine learning statistics

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