A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)
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Updated
May 22, 2024 - Python
A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)
ELKI Data Mining Toolkit
🌲 Implementation of the Robust Random Cut Forest algorithm for anomaly detection on streams
🔗 Methods for Correlation Analysis
pca: A Python Package for Principal Component Analysis.
Anomaly detection using LoOP: Local Outlier Probabilities, a local density based outlier detection method providing an outlier score in the range of [0,1].
Open-source framework to detect outliers in Elasticsearch events
2D Outlier Analysis using Shiny
RADseq Data Exploration, Manipulation and Visualization using R
Streaming Anomaly Detection Framework in Python (Outlier Detection for Streaming Data)
Deep Learning for Anomaly Deteection
Utility library for detecting and removing outliers from normally distributed datasets using the Smirnov-Grubbs test.
Image Mosaicing or Panorama Creation
An algorithm based on Java implementation, can automatically check the set of outliers in a set of data, eliminate these outliers, and finally get normal data.基于java实现的能够自动检查出一组数据中的异常值的集合,剔除这些异常集,得到正常数据。
Mean and Covariance Matrix Estimation under Heavy Tails
Beyond Outlier Detection: LookOut for Pictorial Explanation
Genie: A Fast and Robust Hierarchical Clustering Algorithm (this R package has now been superseded by genieclust)
Imputation of Financial Time Series with Missing Values and/or Outliers
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