---
title: "Anomaly Mining - Past, Present and Future"
authors:
  - "Leman Akoglu"
doi: "10.24963/ijcai.2021/697"
url: "https://ijcai.org/proceedings/2021/697"
pdf: "https://ijcai.org/proceedings/2021/0697.pdf"
venue: "Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"
year: 2021
pages: "4932-4936"
track: "Early Career"
keywords:
  - "Data Mining: Anomaly/Outlier Detection"
  - "Machine Learning: Deep Learning"
  - "Data Mining: Mining Graphs, Semi Structured Data, Complex Data"
  - "Machine Learning: Unsupervised Learning"
---

# Anomaly Mining - Past, Present and Future

Leman Akoglu

*Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence*, pages 4932-4936. DOI: [10.24963/ijcai.2021/697](https://doi.org/10.24963/ijcai.2021/697)

## Abstract

Anomaly mining is an important problem that finds numerous applications in various real world do- mains 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. 

Disclaimer. I try to provide an overview of past and recent trends in both areas within 4 pages. Undoubtedly, these are my personal view of the trends, which can be organized differently. For brevity, I omit all technical details and refer to corresponding papers. Again, due to space limit, it is not possible to include all (even most relevant) references, but a few representative examples.