yzhao062/SUOD
(MLSys' 21) An Acceleration System for Large-scare Unsupervised Heterogeneous Outlier Detection (Anomaly Detection)
This project helps data analysts and engineers efficiently identify unusual patterns or 'outliers' within very large and complex datasets. It takes in your raw data and, by running multiple anomaly detection methods in parallel, quickly outputs scores or labels indicating which data points are abnormal. This is ideal for professionals needing to spot anomalies in big data, such as in fraud detection or system intrusion monitoring.
393 stars. No commits in the last 6 months. Available on PyPI.
Use this if you need to perform unsupervised outlier detection on large, high-dimensional datasets and want to significantly speed up the process by running multiple detection algorithms simultaneously.
Not ideal if you are working with small datasets or if your outlier detection needs are simple enough for a single model to handle efficiently.
Stars
393
Forks
46
Language
Python
License
BSD-2-Clause
Category
Last pushed
Mar 24, 2025
Commits (30d)
0
Dependencies
9
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