Anomaly Mining - Past, Present and Future

Leman Akoglu

IJCAI 2021poster18 citations

Anomaly Mining - Past, Present and Future

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.

Data Mining: Anomaly/Outlier DetectionData Mining: Mining Graphs, Semi Structured Data, Complex DataMachine Learning: Deep LearningMachine Learning: Unsupervised Learning

BibTeX

@inproceedings{ijcai2021p697,
  title     = {Anomaly Mining - Past, Present and Future},
  author    = {Akoglu, Leman},
  booktitle = {Proceedings of the Thirtieth International Joint Conference on
               Artificial Intelligence, {IJCAI-21}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Zhi-Hua Zhou},
  pages     = {4932--4936},
  year      = {2021},
  month     = {8},
  note      = {Early Career},
  doi       = {10.24963/ijcai.2021/697},
  url       = {https://doi.org/10.24963/ijcai.2021/697},
}