Which unsupervised algorithm is designed for detecting anomalous data points within a dataset?

The Random Cut Forest (RCF) is specifically designed for anomaly detection and excels in identifying outliers in a dataset. It operates by creating multiple trees that segment the feature space of the input data randomly. As it builds these trees, it inherently learns the distribution of the normal data points. When new data is introduced, RCF can effectively evaluate how isolated these points are in relation to the rest of the data. Points that are found to be far more isolated than others are flagged as anomalies, making RCF an effective tool for this type of analysis.

Furthermore, while Isolation Forest is also a robust algorithm for anomaly detection, RCF is particularly noteworthy due to its adaptability to different data distributions and seamless integration with Amazon's machine learning tools. This adaptability and efficiency in managing large datasets further position RCF as a standout choice specifically designed for detecting anomalies.