This repository contains implementations of the Random Cut Forest (RCF) probabilistic data structure. RCFs were originally developed at Amazon to use in a nonparametric anomaly detection algorithm for streaming data. Later new algorithms based on RCFs were developed for density estimation, imputation, and forecasting.
The different directories correspond to equivalent implementations in different languages, and bindings to to those base implementations, using language specific features for greater flexibility of use.
The package randomcutforest-examples showcases several example scenarios for using the repository. They also provide examples for some of the parameter settings. Many of these examples are built in tests.
- Guha, S., Mishra, N., Roy, G., & Schrijvers, O. (2016, June). Robust random cut forest based anomaly detection on streams. In International conference on machine learning (pp. 2712-2721).
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