Anomaly Detection

• AZZEDINE BOUKERCHE and LINING ZHENG, OMAR ALFANDI. Outlier Detection: Methods, Models, and Classification. ACM Comput. Surv., Vol. 53, No. 3, Article 55, Publication date: June 2020.  DOI: https://doi.org/10.1145/3381028 

Recent Articles: 

• CIKM24:

1. Towards Online and Safe Configuration Tuning with Semi-supervised Anomaly Detection [3627673.3679700]

2. Ensembles for Outlier Detection and Evaluation [3627673.3679755]

3. Rethinking Temporal Graph Transformers for Outlier Detection

4. Out-of-Distribution Aware Classification for Tabular Data [3627673.3679060]

5. Transformer for Point Anomaly Detection [3627673.3679859]

6. DetCat: Detecting Categorical Outliers in Relational Datasets [3627673.3679212]

• SIGKD23:

1. The Need for Unsupervised Outlier Model Selection: A Review and Evaluation of Internal Evaluation Strategies [3606274.3606277]

2. Open-Set Semi-Supervised Text Classification with Latent Outlier Softening [3580305.3599456]

• OTHERS:

1. [JMLR 2024] PyGOD: A Python Library for Graph Outlier Detection [23-0963]

2. [2024] Systematic Literature Review of Spatio-Temporal Graph Neural Network Models for Time Series Forecasting and Classification [2410.22377v1]

3. [ICLR 2024] FEDOD: FEDERATED OUTLIER DETECTION VIA NEURAL APPROXIMATION

4. [TMLR 2024] A Distance-based Anomaly Detection Framework for Deep Reinforcement Learning

5. [Elsevier 2023] Meta-survey on outlier and anomaly detection [1-s2.0-S0925231223007579-am]

6. [EACL 2023] Unsupervised Anomaly Detection in Multi-Topic Short-Text Corpora

7. [NeurIPS 2022] BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs

8. [ACM WSDM 2022] Understanding and Mitigating the Effect of Outliers in Fair Ranking [3488560.3498441]

9. [ACM WWW 2022] Outlier Detection for Streaming Task Assignment in Crowdsourcing [3485447.3512067]

10. [Elsevier 2020] A critical overview of outlier detection methods

11. [ACM 2020] Outlier Detection: Methods, Models, and Classification [3381028]

12. [IEEE 2019] Progress in Outlier Detection Techniques: A Survey

Classical Articles

• Chen, J., Sathe, S., Aggarwal, C., & Turaga, D. (2017). Outlier detection with autoencoder ensembles. In Proceedings of SIAM international conference on data mining (pp. 90–98). 

• Varun Chandola, Arindam Banerjee and Vipin Kumar. Anomaly detection: A survey, ACM Computing Surveys (CSUR), Volume 41 Issue 3, July 2009, Article No. 15. (pdf) 

• Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng, Jörg Sander. LOF: Identifying Density-Based Local Outliers, Proc. ACM SIGMOD 2000 Int. Conf. On Management of Data, Dalles, TX, 2000. (LOF)    

• Wen Jin, Anthony K. H. Tung, Jiawei Han, Wei Wang.  Ranking Outliers Using Symmetric Neighborhood Relationship,  PAKDD 2006. (INFLO)

• Ke Zhang, Marcus Hutter and  Huidong Jin.  A New Local Distance-Based Outlier Detection Approach for Scattered Real-World Data, PAKDD 2009 . (LDOF)

Streaming Algorithms

• 1. Yanwei Yu, Lei Cao, Elke A. Rundensteiner, Qin Wang. Outlier Detection over Massive-Scale Trajectory Streams, ACM TODS (June,2018)                                               

  2. Yanwei Yu, Lei Cao, Elke A. Rundensteiner, Qin Wang. Detecting Moving Object Outliers In Massive-Scale Trajectory Streams, KDD 2014.            

  3.  Dragoljub Pokrajac, Aleksandar Lazarevic and  Longin Jan Latecki. Incremental Local Outlier Detection for Data Streams,  IEEE Symposium on Computational Intelligence and Data Mining, 2007. CIDM 2007.