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

Robust Random Cut Forest

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.

University of Michigan - Ann Arbor

Google

- Isolation forest
- Robust random cut forest 
- Boxplots/Statistical Approach


Ways of detecting outliers

https://towardsdatascience.com/5-ways-to-detect-outliers-that-every-data-scientist-should-know-python-code-70a54335a623
https://towardsdatascience.com/dbscan-algorithm-complete-guide-and-application-with-python-scikit-learn-d690cbae4c5d

Isolation forests isolate outliers found using anomaly detection. This is adverse to other techniques that typically use data profiling (which are statistical methods used to describe the data set as a whole).


Isolation  Forest

The boxplot method is a statistical approach for detecting outliers that visualizes the distribution and spread of data. It identifies anomalies by marking any data points that fall beyond the whiskers of the plot.

Learn Before

Related

Learn After