University of Michigan - Ann Arbor

A robust random cut forest is an anomaly detection algorithm that isolates outliers through association. It assigns an anomaly score to data points, categorizing them as 'normal', 'low', or 'high'. The algorithm is effective for identifying anomalies in both offline and streaming data.

Robust random cut forest algorithm

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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