mlsanityData to 7 Oct 2026

Anomaly Mining -- Past, Present and Future

arXiv 2105.10077 ↗First posted 21 May 2021cs.LG cs.SIcited by 0

Not on the map. Why?

Abstract (arXiv)

Anomaly mining is an important problem that finds numerous applications in various real world domains such as environmental monitoring, cybersecurity, finance, healthcare and medicine, to name a few. In this article, I focus on two areas, (1) point-cloud and (2) graph-based anomaly mining. I aim to present a broad view of each area, and discuss classes of main research problems, recent trends and future directions. I conclude with key take-aways and overarching open problems.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 15 of its 61 references that are in the map. First posted on arXiv 21 May 2021.

Builds on

15 of 61 references.

References · 15

  1. 2020A Unifying Review of Deep and Shallow Anomaly Detectioncited by 423
  2. 2019Deep Learning for Anomaly Detection: A Surveycited by 357
  3. 2014Graph-based Anomaly Detection and Description: A Surveycited by 182
  4. 2020Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progresscited by 148
  5. 2019Causal structure based root cause analysis of outlierscited by 65
  6. 2021Few-shot Network Anomaly Detection via Cross-network Meta-learningcited by 64
  7. 2020On Using Classification Datasets to Evaluate Graph-Level Outlier Detection: Peculiar Observations and New Insightscited by 53
  8. 2015A Meta-Analysis of the Anomaly Detection Problemcited by 23
  9. 2017Contextual Outlier Interpretationcited by 19
  10. 2017Explaining Anomalies in Groups with Characterizing Subspace Rulescited by 13
  11. 2020Automating Outlier Detection via Meta-Learningcited by 9
  12. 2014Spotting Suspicious Link Behavior with fBox: An Adversarial Perspectivecited by 6
3 more
  1. 2020One-Class Graph Neural Networks for Anomaly Detection in Attributed Networkscited by 6
  2. 2020Anomaly Detection in Large Labeled Multi-Graph Databasescited by 5
  3. 2020AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learningcited by 3

Built on by

Not cited yet.