University of Michigan - Ann Arbor

Robust random cut forests also isolate outliers, however they do so through association. It associates a score to an anomaly, thereby analyzing these scores on a basis of “normal”, “low” or “high”. It works extremely well on robust data. 


Robust random cut forests 


This is actually amazon’s algorithm used to detect anomalies. It works with current streaming data as well as offline data. 

Robust random cut forest applications

Robust random cut forest code example

http://proceedings.mlr.press/v48/guha16.html
http://proceedings.mlr.press/v48/guha16.pdf

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