A curated list of graph-based fraud, anomaly, and outlier detection papers & resources
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Updated
May 28, 2024
A curated list of graph-based fraud, anomaly, and outlier detection papers & resources
🔴 MiniSom is a minimalistic implementation of the Self Organizing Maps
FRASER - Find RAre Splicing Events in RNA-seq
A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)
RedBorder Outliers Detector with Keras
Deep learning-based outlier/anomaly detection
The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
JuliaGrid is an easy-to-use power system simulation tool for researchers and educators provided as a Julia package.
SERoW Framework for N Legged Robot Walking Estimation
Time series processing library
Client interface for all things Cleanlab Studio
SimAD, deep learning, anomaly detection, outlier detection, time series
PatchAD, deep learning, anomaly detection, outlier detection, time series
Data science include Data Analysis, Machine learning , EDA,PCA and Data Structure and Algorithms
Collection of operational time series ML models and tools
The DOMID (Detecting Outliers in MIxed-type Data) R package includes functions that can be used for detecting outliers in data sets consisting of mixed-type data (i.e. both continuous and discrete variables).
Official repository for “PATE: Proximity-Aware Time series anomaly Evaluation”.
This repository hosts an R script tailored for preprocessing and cleansing LiDAR (Light Detection and Ranging) LAS files, specifically targeting data from the year 2020. Primarily, the script centers on outlier removal within point cloud data through statistical thresholding. Its workflow encompasses LAS file parsing, Z-based box plot generation.
This repo contains projects, tasks and other code which I have developed on a Data Scientist course at SkillFactory.
(Python, R, C/C++) Isolation Forest and variations such as SCiForest and EIF, with some additions (outlier detection + similarity + NA imputation)
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