rrcf: Implementation of the Robust Random Cut Forest algorithm for anomaly detection on streams

Python Submitted 04 March 2019Published 29 March 2019
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Editor: @VivianePons (all papers)
Reviewers: @vc1492a (all reviews), @justinshenk (all reviews)

Authors

Matthew D. Bartos (0000-0001-6421-222X), Abhiram Mullapudi (0000-0001-8141-3621), Sara C. Troutman (0000-0002-6809-7959)

Citation

Bartos et al., (2019). rrcf: Implementation of the Robust Random Cut Forest algorithm for anomaly detection on streams. Journal of Open Source Software, 4(35), 1336, https://doi.org/10.21105/joss.01336

@article{Bartos2019, doi = {10.21105/joss.01336}, url = {https://doi.org/10.21105/joss.01336}, year = {2019}, publisher = {The Open Journal}, volume = {4}, number = {35}, pages = {1336}, author = {Matthew D. Bartos and Abhiram Mullapudi and Sara C. Troutman}, title = {rrcf: Implementation of the Robust Random Cut Forest algorithm for anomaly detection on streams}, journal = {Journal of Open Source Software} }
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outlier detection machine learning ensemble methods random forests

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ISSN 2475-9066