# Unsupervised anomaly detection algorithms on real-world data: how many do we need?

arXiv: https://arxiv.org/abs/2305.00735 · HTML: https://mlsanity.com/paper/2305.00735/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 59 references that are in the map.
- First posted on arXiv 1 May 2023.

## Builds on (most cited first, 17 of 17)

- [Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering](https://mlsanity.com/paper/1602.01585/) (2016)
- [A Unifying Review of Deep and Shallow Anomaly Detection](https://mlsanity.com/paper/2009.11732/) (2020)
- [Deep Semi-Supervised Anomaly Detection](https://mlsanity.com/paper/1906.02694/) (2019)
- [ADBench: Anomaly Detection Benchmark](https://mlsanity.com/paper/2206.09426/) (2022)
- [PyOD: A Python Toolbox for Scalable Outlier Detection](https://mlsanity.com/paper/1901.01588/) (2019)
- [ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions](https://mlsanity.com/paper/2201.00382/) (2022)
- [COPOD: Copula-Based Outlier Detection](https://mlsanity.com/paper/2009.09463/) (2020)
- [Adversarially Learned Anomaly Detection](https://mlsanity.com/paper/1812.02288/) (2018)
- [Extended Isolation Forest](https://mlsanity.com/paper/1811.02141/) (2018)
- [Generative Adversarial Active Learning for Unsupervised Outlier Detection](https://mlsanity.com/paper/1809.10816/) (2018)
- [LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks](https://mlsanity.com/paper/2112.05355/) (2021)
- [Benchmarking Unsupervised Outlier Detection with Realistic Synthetic Data](https://mlsanity.com/paper/2004.06947/) (2020)
- [A Meta-Analysis of the Anomaly Detection Problem](https://mlsanity.com/paper/1503.01158/) (2015)
- [On the Nature and Types of Anomalies: A Review of Deviations in Data](https://mlsanity.com/paper/2007.15634/) (2020)
- [ELKI: A large open-source library for data analysis - ELKI Release 0.7.5 "Heidelberg"](https://mlsanity.com/paper/1902.03616/) (2019)
- [Unsupervised anomaly localization using VAE and beta-VAE](https://mlsanity.com/paper/2005.10686/) (2020)
- [gen2Out: Detecting and Ranking Generalized Anomalies](https://mlsanity.com/paper/2109.02704/) (2021)

## Built on by (most cited first, 20 of 20)

- [Autoencoders for Anomaly Detection are Unreliable](https://mlsanity.com/paper/2501.13864/) (2025)
- [Reconstruction Error-based Anomaly Detection with Few Outlying Examples](https://mlsanity.com/paper/2305.10464/) (2023)
- [Enhancing Interpretability and Generalizability in Extended Isolation Forests](https://mlsanity.com/paper/2310.05468/) (2023)
- [We Need to Rethink Benchmarking in Anomaly Detection](https://mlsanity.com/paper/2507.15584/) (2025)
- [ICLAD: In-Context Learning for Unified Tabular Anomaly Detection Across Supervision Regimes](https://mlsanity.com/paper/2603.19497/) (2026)
- [Foundation Models for Anomaly Detection: Vision and Challenges](https://mlsanity.com/paper/2502.06911/) (2025)
- [An Evidence-Based Post-Hoc Adjustment Framework for Anomaly Detection Under Data Contamination](https://mlsanity.com/paper/2510.21296/) (2025)
- [FoMo X: Modular Explainability Signals for Outlier Detection Foundation Models](https://mlsanity.com/paper/2603.17570/) (2026)
- [Harmonic Machine Learning Models are Robust](https://mlsanity.com/paper/2404.18825/) (2024)
- [Fairness-aware Anomaly Detection via Fair Projection](https://mlsanity.com/paper/2505.11132/) (2025)
- [Towards Transparent and Efficient Anomaly Detection in Industrial Processes through ExIFFI](https://mlsanity.com/paper/2405.01158/) (2024)
- [Acquiring Better Load Estimates by Combining Anomaly and Change Point Detection in Power Grid Time-series Measurements](https://mlsanity.com/paper/2405.16164/) (2024)
- [Real-Time Anomaly Detection with Synthetic Anomaly Monitoring (SAM)](https://mlsanity.com/paper/2501.18417/) (2025)
- [Unsupervised anomaly detection on cybersecurity data streams: a case with BETH dataset](https://mlsanity.com/paper/2503.04178/) (2025)
- [Real-Time Decorrelation-Based Anomaly Detection for Multivariate Time Series](https://mlsanity.com/paper/2507.07559/) (2025)
- [Dynamic Reward Scaling for Multivariate Time Series Anomaly Detection: A VAE-Enhanced Reinforcement Learning Approach](https://mlsanity.com/paper/2511.12351/) (2025)
- [A Trainable Centrality Framework for Modern Data](https://mlsanity.com/paper/2511.22959/) (2025)
- [Novelty detection on path space](https://mlsanity.com/paper/2512.03243/) (2025)
- [GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges](https://mlsanity.com/paper/2605.07133/) (2026)
- [When Identical Rows Disagree: From Benchmark Identifiability to Replication-Robust Anomaly Detection](https://mlsanity.com/paper/2609.29580/) (2026)

## Abstract (arXiv)

In this study we evaluate 32 unsupervised anomaly detection algorithms on 52 real-world multivariate tabular datasets, performing the largest comparison of unsupervised anomaly detection algorithms to date. On this collection of datasets, the k-thNN (distance to the k-nearest neighbor) algorithm significantly outperforms the most other algorithms. Visualizing and then clustering the relative performance of the considered algorithms on all datasets, we identify two clear clusters: one with ``local'' datasets, and another with ``global'' datasets. ``Local'' anomalies occupy a region with low density when compared to nearby samples, while ``global'' occupy an overall low density region in the feature space. On the local datasets the kNN (k-nearest neighbor) algorithm comes out on top. On the global datasets, the EIF (extended isolation forest) algorithm performs the best. Also taking into consideration the algorithms' computational complexity, a toolbox with these three unsupervised anomaly detection algorithms suffices for finding anomalies in this representative collection of multivariate datasets. By providing access to code and datasets, our study can be easily reproduced and extended with more algorithms and/or datasets.