kLabUM/rrcf
🌲 Implementation of the Robust Random Cut Forest algorithm for anomaly detection on streams
This helps data analysts and operations engineers pinpoint unusual behavior or system issues in real-time. It takes a continuous stream of data points, like sensor readings, server logs, or financial transactions, and flags any data that looks out of place. The output is a clear score indicating how anomalous each new data point is, helping you quickly spot and investigate problems as they occur.
521 stars. No commits in the last 6 months.
Use this if you need to detect anomalies or outliers in high-dimensional, continuously flowing data streams, where quick identification of unusual patterns is crucial.
Not ideal if your data is static and small, or if you need to explain the anomaly in terms of complex interdependencies rather than just its statistical rarity.
Stars
521
Forks
116
Language
Python
License
MIT
Category
Last pushed
Feb 24, 2024
Commits (30d)
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