# SageMaker Random Cut Forest and Anomaly Detection

An introductory notebook on using Amazon SageMaker to train and use random cut
forest models to perform anomaly detection.

<p align="center">
<img src="https://github.com/awslabs/amazon-sagemaker-examples/blob/master/introduction_to_amazon_algorithms/random_cut_forest/img/rcf.png">
</p>

## References

For more details about random cut forests and information about the algorithm
used in the Amazon SageMaker RCF algorithm consult the following papers:

* Sudipto Guha, Nina Mishra, Gourav Roy, and Okke Schrijvers. *"Robust random
  cut forest based anomaly detection on streams."* In International Conference
  on Machine Learning, pp. 2712-2721. 2016.
* Byung-Hoon Park, George Ostrouchov, Nagiza F. Samatova, and Al Geist.
  *"Reservoir-based random sampling with replacement from data stream."* In
  Proceedings of the 2004 SIAM International Conference on Data Mining, pp.
  492-496. Society for Industrial and Applied Mathematics, 2004.