Outlier Detection (also known as Anomaly Detection) is an exciting yet challenging field, which aims to identify outlying objects that are deviant from the general data distribution. Outlier detection has been proven critical in many fields, such as credit card fraud analytics, network intrusion detection, and mechanical unit defect detection.
This repository collects:
- Books & Academic Papers
- Online Courses and Videos
- Outlier Datasets
- Open-source and Commercial Libraries/Toolkits
- Key Conferences & Journals
More items will be added to the repository. Please feel free to suggest other key resources by opening an issue report, submitting a pull request, or dropping me an email @ ([email protected]). Enjoy reading!
- 1. Books & Tutorials
- 2. Courses/Seminars/Videos
- 3. Toolbox & Datasets
- 4. Papers
- 4.1. Overview & Survey Papers
- 4.2. Key Algorithms
- 4.3. Graph & Network Outlier Detection
- 4.4. Time Series Outlier Detection
- 4.5. Feature Selection in Outlier Detection
- 4.6. High-dimensional & Subspace Outliers
- 4.7. Outlier Ensembles
- 4.8. Outlier Detection in Evolving Data
- 4.9. Representation Learning in Outlier Detection
- 4.10. Interpretability
- 4.11. Outlier Detection with Neural Networks
- 4.12. Active Anomaly Detection
- 4.13. Interactive Outlier Detection
- 4.14. Outlier Detection in Other fields
- 4.15. Outlier Detection Applications
- 4.16. Automated Outlier Detection
- 4.17. Emerging and Interesting Topics
- 5. Key Conferences/Workshops/Journals
Outlier Analysis by Charu Aggarwal: Classical text book covering most of the outlier analysis techniques. A must-read for people in the field of outlier detection. [Preview.pdf]
Outlier Ensembles: An Introduction by Charu Aggarwal and Saket Sathe: Great intro book for ensemble learning in outlier analysis.
Data Mining: Concepts and Techniques (3rd) by Jiawei Han and Micheline Kamber and Jian Pei: Chapter 12 discusses outlier detection with many key points. [Google Search]
Tutorial Title | Venue | Year | Ref | Materials |
---|---|---|---|---|
Data mining for anomaly detection | PKDD | 2008 | [38] | [Video] |
Outlier detection techniques | ACM SIGKDD | 2010 | [35] | [PDF] |
Anomaly Detection: A Tutorial | ICDM | 2011 | [17] | [PDF] |
Anomaly Detection in Networks | KDD | 2017 | [48] | [Page] |
Which Anomaly Detector should I use? | ICDM | 2018 | [67] | [PDF] |
Coursera Introduction to Anomaly Detection (by IBM): [See Video]
Coursera Real-Time Cyber Threat Detection and Mitigation partly covers the topic: [See Video]
Coursera Machine Learning by Andrew Ng also partly covers the topic:
Udemy Outlier Detection Algorithms in Data Mining and Data Science: [See Video]
Stanford Data Mining for Cyber Security also covers part of anomaly detection techniques: [See Video]
[Python] Python Outlier Detection (PyOD): PyOD is a comprehensive and scalable Python toolkit for detecting outlying objects in multivariate data. It contains more than 20 detection algorithms, including emerging deep learning models and outlier ensembles.
[Python] Python Streaming Anomaly Detection (PySAD): PySAD is a streaming anomaly detection framework in Python, which provides a complete set of tools for anomaly detection experiments. It currently contains more than 15 online anomaly detection algorithms and 2 different methods to integrate PyOD detectors to the streaming setting.
[Python] Scikit-learn Novelty and Outlier Detection. It supports some popular algorithms like LOF, Isolation Forest, and One-class SVM.
[Python] Scalable Unsupervised Outlier Detection (SUOD): SUOD (Scalable Unsupervised Outlier Detection) is an acceleration framework for large-scale unsupervised outlier detector training and prediction, on top of PyOD.
[Java] ELKI: Environment for Developing KDD-Applications Supported by Index-Structures: ELKI is an open source (AGPLv3) data mining software written in Java. The focus of ELKI is research in algorithms, with an emphasis on unsupervised methods in cluster analysis and outlier detection.
[Java] RapidMiner Anomaly Detection Extension: The Anomaly Detection Extension for RapidMiner comprises the most well know unsupervised anomaly detection algorithms, assigning individual anomaly scores to data rows of example sets. It allows you to find data, which is significantly different from the normal, without the need for the data being labeled.
[R] outliers package: A collection of some tests commonly used for identifying outliers in R.
[Matlab] Anomaly Detection Toolbox - Beta: A collection of popular outlier detection algorithms in Matlab.
[Python] TODS: TODS is a full-stack automated machine learning system for outlier detection on multivariate time-series data.
[Python] skyline: Skyline is a near real time anomaly detection system.
[Python] banpei: Banpei is a Python package of the anomaly detection.
[Python] telemanom: A framework for using LSTMs to detect anomalies in multivariate time series data.
[Python] DeepADoTS: A benchmarking pipeline for anomaly detection on time series data for multiple state-of-the-art deep learning methods.
[Python] NAB: The Numenta Anomaly Benchmark: NAB is a novel benchmark for evaluating algorithms for anomaly detection in streaming, real-time applications.
[R] AnomalyDetection: AnomalyDetection is an open-source R package to detect anomalies which is robust, from a statistical standpoint, in the presence of seasonality and an underlying trend.
[R] anomalize: The 'anomalize' package enables a "tidy" workflow for detecting anomalies in data.
[Open Distro] Real Time Anomaly Detection in Open Distro for Elasticsearch by Amazon: A machine learning-based anomaly detection plugins for Open Distro for Elasticsearch. See Real Time Anomaly Detection in Open Distro for Elasticsearch.
[Python] datastream.io: An open-source framework for real-time anomaly detection using Python, Elasticsearch and Kibana.
ELKI Outlier Datasets: https://elki-project.github.io/datasets/outlier
Outlier Detection DataSets (ODDS): http://odds.cs.stonybrook.edu/#table1
Unsupervised Anomaly Detection Dataverse: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/OPQMVF
Anomaly Detection Meta-Analysis Benchmarks: https://ir.library.oregonstate.edu/concern/datasets/47429f155
Paper Title | Venue | Year | Ref | Materials |
---|---|---|---|---|
A survey of outlier detection methodologies | ARTIF INTELL REV | 2004 | [32] | [PDF] |
Anomaly detection: A survey | CSUR | 2009 | [16] | [PDF] |
A meta-analysis of the anomaly detection problem | Preprint | 2015 | [24] | [PDF] |
On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study | DMKD | 2016 | [11] | [HTML], [SLIDES] |
A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data | PLOS ONE | 2016 | [28] | [PDF] |
A comparative evaluation of outlier detection algorithms: Experiments and analyses | Pattern Recognition | 2018 | [23] | [PDF] |
Research Issues in Outlier Detection | Book Chapter | 2019 | [65] | [HTML] |
Quantitative comparison of unsupervised anomaly detection algorithms for intrusion detection | SAC | 2019 | [26] | [HTML] |
Progress in Outlier Detection Techniques: A Survey | IEEE Access | 2019 | [69] | [PDF] |
Deep learning for anomaly detection: A survey | Preprint | 2019 | [15] | [PDF] |
Anomalous Instance Detection in Deep Learning: A Survey | Preprint | 2020 | [10] | [PDF] |
Deep Learning for Anomaly Detection: A Review | Preprint | 2020 | [54] | [PDF] |
Abbreviation | Paper Title | Venue | Year | Ref | Materials |
---|---|---|---|---|---|
kNN | Efficient algorithms for mining outliers from large data sets | ACM SIGMOD Record | 2000 | [57] | [PDF] |
KNN | Fast outlier detection in high dimensional spaces | PKDD | 2002 | [6] | [PDF] |
LOF | LOF: identifying density-based local outliers | ACM SIGMOD Record | 2000 | [9] | [PDF] |
IForest | Isolation forest | ICDM | 2008 | [41] | [PDF] |
OCSVM | Estimating the support of a high-dimensional distribution | Neural Computation | 2001 | [63] | [PDF] |
AutoEncoder Ensemble | Outlier detection with autoencoder ensembles | SDM | 2017 | [18] | [PDF] |
COPOD | COPOD: Copula-Based Outlier Detection | ICDM | 2020 | [40] | [PDF] |
Paper Title | Venue | Year | Ref | Materials |
---|---|---|---|---|
Graph based anomaly detection and description: a survey | DMKD | 2015 | [5] | [PDF] |
Anomaly detection in dynamic networks: a survey | WIREs Computational Statistic | 2015 | [58] | [PDF] |
Outlier detection in graphs: On the impact of multiple graph models | ComSIS | 2019 | [13] | [PDF] |
Paper Title | Venue | Year | Ref | Materials |
---|---|---|---|---|
Outlier detection for temporal data: A survey | TKDE | 2014 | [29] | [PDF] |
Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding | KDD | 2018 | [33] | [PDF], [Code] |
Time-Series Anomaly Detection Service at Microsoft | KDD | 2019 | [59] | [PDF] |
Paper Title | Venue | Year | Ref | Materials |
---|---|---|---|---|
Unsupervised feature selection for outlier detection by modelling hierarchical value-feature couplings | ICDM | 2016 | [51] | [PDF] |
Learning homophily couplings from non-iid data for joint feature selection and noise-resilient outlier detection | IJCAI | 2017 | [52] | [PDF] |
Paper Title | Venue | Year | Ref | Materials |
---|---|---|---|---|
A survey on unsupervised outlier detection in high-dimensional numerical data | Stat Anal Data Min | 2012 | [81] | [HTML] |
Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection | SIGKDD | 2018 | [53] | [PDF] |
Reverse Nearest Neighbors in Unsupervised Distance-Based Outlier Detection | TKDE | 2015 | [56] | [PDF], [SLIDES] |
Outlier detection for high-dimensional data | Biometrika | 2015 | [61] | [PDF] |
Paper Title | Venue | Year | Ref | Materials |
---|---|---|---|---|
Outlier ensembles: position paper | SIGKDD Explorations | 2013 | [2] | [PDF] |
Ensembles for unsupervised outlier detection: challenges and research questions a position paper | SIGKDD Explorations | 2014 | [82] | [PDF] |
An Unsupervised Boosting Strategy for Outlier Detection Ensembles | PAKDD | 2018 | [12] | [HTML] |
LSCP: Locally selective combination in parallel outlier ensembles | SDM | 2019 | [77] | [PDF] |
Paper Title | Venue | Year | Ref | Materials |
---|---|---|---|---|
A Survey on Anomaly detection in Evolving Data: [with Application to Forest Fire Risk Prediction] | SIGKDD Explorations | 2018 | [62] | [PDF] |
Unsupervised real-time anomaly detection for streaming data | Neurocomputing | 2017 | [4] | [PDF] |
Outlier Detection in Feature-Evolving Data Streams | SIGKDD | 2018 | [47] | [PDF], [Github] |
Evaluating Real-Time Anomaly Detection Algorithms--The Numenta Anomaly Benchmark | ICMLA | 2015 | [37] | [PDF], [Github] |
MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams | AAAI | 2020 | [8] | [PDF], [Github] |
NETS: Extremely Fast Outlier Detection from a Data Stream via Set-Based Processing | VLDB | 2019 | [71] | [PDF], [Github], [Slide] |
Ultrafast Local Outlier Detection from a Data Stream with Stationary Region Skipping | KDD | 2020 | [72] | [PDF], [Github], [Slide] |
Paper Title | Venue | Year | Ref | Materials |
---|---|---|---|---|
Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection | SIGKDD | 2018 | [53] | [PDF] |
Learning representations for outlier detection on a budget | Preprint | 2015 | [49] | [PDF] |
XGBOD: improving supervised outlier detection with unsupervised representation learning | IJCNN | 2018 | [76] | [PDF] |
Paper Title | Venue | Year | Ref | Materials |
---|---|---|---|---|
Explaining Anomalies in Groups with Characterizing Subspace Rules | DMKD | 2018 | [46] | [PDF] |
Beyond Outlier Detection: LookOut for Pictorial Explanation | ECML-PKDD | 2018 | [50] | [PDF] |
Contextual outlier interpretation | IJCAI | 2018 | [43] | [PDF] |
Mining multidimensional contextual outliers from categorical relational data | IDA | 2015 | [66] | [PDF] |
Discriminative features for identifying and interpreting outliers | ICDE | 2014 | [19] | [PDF] |
Sequential Feature Explanations for Anomaly Detection | TKDD | 2019 | [64] | [HTML] |
Paper Title | Venue | Year | Ref | Materials |
---|---|---|---|---|
Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding | KDD | 2018 | [33] | [PDF], [Code] |
MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks | Preprint | 2019 | [39] | [PDF], [Code] |
Generative Adversarial Active Learning for Unsupervised Outlier Detection | TKDE | 2019 | [44] | [PDF], [Code] |
Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection | ICLR | 2018 | [83] | [PDF], [Code] |
Deep Anomaly Detection with Outlier Exposure | ICLR | 2019 | [31] | [PDF], [Code] |
Unsupervised Anomaly Detection With LSTM Neural Networks | IEEE TNNLS | 2019 | [25] | [PDF], [IEEE], |
Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative Network | NeurIPS | 2019 | [68] | [PDF] [Code] |
Paper Title | Venue | Year | Ref | Materials |
---|---|---|---|---|
Active learning for anomaly and rare-category detection | NeurIPS | 2005 | [55] | [PDF] |
Outlier detection by active learning | SIGKDD | 2006 | [1] | [PDF] |
Active Anomaly Detection via Ensembles: Insights, Algorithms, and Interpretability | Preprint | 2019 | [20] | [PDF] |
Meta-AAD: Active Anomaly Detection with Deep Reinforcement Learning | ICDM | 2020 | [75] | [PDF] |
Paper Title | Venue | Year | Ref | Materials |
---|---|---|---|---|
Learning On-the-Job to Re-rank Anomalies from Top-1 Feedback | SDM | 2019 | [36] | [PDF] |
Interactive anomaly detection on attributed networks | WSDM | 2019 | [21] | [PDF] |
eX2: a framework for interactive anomaly detection | IUI Workshop | 2019 | [7] | [PDF] |
Tripartite Active Learning for Interactive Anomaly Discovery | IEEE Access | 2019 | [80] | [PDF] |
Field | Paper Title | Venue | Year | Ref | Materials |
---|---|---|---|---|---|
Text | Outlier detection for text data | SDM | 2017 | [34] | [PDF] |
Field | Paper Title | Venue | Year | Ref | Materials |
---|---|---|---|---|---|
Security | A survey of distance and similarity measures used within network intrusion anomaly detection | IEEE Commun. Surv. Tutor. | 2015 | [70] | [PDF] |
Security | Anomaly-based network intrusion detection: Techniques, systems and challenges | Computers & Security | 2009 | [27] | [PDF] |
Finance | A survey of anomaly detection techniques in financial domain | Future Gener Comput Syst | 2016 | [3] | [PDF] |
Traffic | Outlier Detection in Urban Traffic Data | WIMS | 2018 | [22] | [PDF] |
Social Media | A survey on social media anomaly detection | SIGKDD Explorations | 2016 | [74] | [PDF] |
Social Media | GLAD: group anomaly detection in social media analysis | TKDD | 2015 | [73] | [PDF] |
Machine Failure | Detecting the Onset of Machine Failure Using Anomaly Detection Methods | DAWAK | 2019 | [60] | [PDF] |
Video Surveillance | AnomalyNet: An anomaly detection network for video surveillance | TIFS | 2019 | [79] | [IEEE], Code |
Paper Title | Venue | Year | Ref | Materials |
---|---|---|---|---|
AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning | ICDE | 2020 | [45] | [PDF] |
Extended Isolation Forest | Preprint | 2020 | [78] | [PDF], [Code] |
Paper Title | Venue | Year | Ref | Materials |
---|---|---|---|---|
Clustering with Outlier Removal | Preprint | 2018 | [42] | [PDF] |
Extended Isolation Forest | TKDE | 2019 | [30] | [PDF] |
Real-World Anomaly Detection by using Digital Twin Systems and Weakly-Supervised Learning | IEEE Trans. Ind. Informat. | 2020 | [14] | [PDF] |
Key data mining conference deadlines, historical acceptance rates, and more can be found data-mining-conferences.
ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD). Note: SIGKDD usually has an Outlier Detection Workshop (ODD), see ODD 2018.
ACM International Conference on Management of Data (SIGMOD)
IEEE International Conference on Data Mining (ICDM)
SIAM International Conference on Data Mining (SDM)
IEEE International Conference on Data Engineering (ICDE)
ACM InternationalConference on Information and Knowledge Management (CIKM)
ACM International Conference on Web Search and Data Mining (WSDM)
The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)
ACM Transactions on Knowledge Discovery from Data (TKDD)
IEEE Transactions on Knowledge and Data Engineering (TKDE)
ACM SIGKDD Explorations Newsletter
Data Mining and Knowledge Discovery
Knowledge and Information Systems (KAIS)
[1] | Abe, N., Zadrozny, B. and Langford, J., 2006, August. Outlier detection by active learning. In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 504-509, ACM. |
[2] | Aggarwal, C.C., 2013. Outlier ensembles: position paper. ACM SIGKDD Explorations Newsletter, 14(2), pp.49-58. |
[3] | Ahmed, M., Mahmood, A.N. and Islam, M.R., 2016. A survey of anomaly detection techniques in financial domain. Future Generation Computer Systems, 55, pp.278-288. |
[4] | Ahmad, S., Lavin, A., Purdy, S. and Agha, Z., 2017. Unsupervised real-time anomaly detection for streaming data. Neurocomputing, 262, pp.134-147. |
[5] | Akoglu, L., Tong, H. and Koutra, D., 2015. Graph based anomaly detection and description: a survey. Data Mining and Knowledge Discovery, 29(3), pp.626-688. |
[6] | Angiulli, F. and Pizzuti, C., 2002, August. Fast outlier detection in high dimensional spaces. In European Conference on Principles of Data Mining and Knowledge Discovery, pp. 15-27. |
[7] | Arnaldo, I., Veeramachaneni, K. and Lam, M., 2019. ex2: a framework for interactive anomaly detection. In ACM IUI Workshop on Exploratory Search and Interactive Data Analytics (ESIDA). |
[8] | Bhatia, S., Hooi, B., Yoon, M., Shin, K. and Faloutsos. C., 2020. MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams. In AAAI Conference on Artificial Intelligence (AAAI). |
[9] | Breunig, M.M., Kriegel, H.P., Ng, R.T. and Sander, J., 2000, May. LOF: identifying density-based local outliers. ACM SIGMOD Record, 29(2), pp. 93-104. |
[10] | Bulusu, S., Kailkhura, B., Li, B., Varshney, P.K. and Song, D., 2020. Anomalous Instance Detection in Deep Learning: A Survey. arXiv preprint arXiv:2003.06979. |
[11] | Campos, G.O., Zimek, A., Sander, J., Campello, R.J., Micenková, B., Schubert, E., Assent, I. and Houle, M.E., 2016. On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study. Data Mining and Knowledge Discovery, 30(4), pp.891-927. |
[12] | Campos, G.O., Zimek, A. and Meira, W., 2018, June. An Unsupervised Boosting Strategy for Outlier Detection Ensembles. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 564-576). Springer, Cham. |
[13] | Campos, G.O., Moreira, E., Meira Jr, W. and Zimek, A., 2019. Outlier Detection in Graphs: A Study on the Impact of Multiple Graph Models. Computer Science & Information Systems, 16(2). |
[14] | Castellani, A., Schmitt, S., Squartini, S., 2020. Real-World Anomaly Detection by using Digital Twin Systems and Weakly-Supervised Learning. In IEEE Transactions on Industrial Informatics. |
[15] | Chalapathy, R. and Chawla, S., 2019. Deep learning for anomaly detection: A survey. arXiv preprint arXiv:1901.03407. |
[16] | Chandola, V., Banerjee, A. and Kumar, V., 2009. Anomaly detection: A survey. ACM computing surveys , 41(3), p.15. |
[17] | Chawla, S. and Chandola, V., 2011, Anomaly Detection: A Tutorial. Tutorial at ICDM 2011. |
[18] | Chen, J., Sathe, S., Aggarwal, C. and Turaga, D., 2017, June. Outlier detection with autoencoder ensembles. SIAM International Conference on Data Mining, pp. 90-98. Society for Industrial and Applied Mathematics. |
[19] | Dang, X.H., Assent, I., Ng, R.T., Zimek, A. and Schubert, E., 2014, March. Discriminative features for identifying and interpreting outliers. In International Conference on Data Engineering (ICDE). IEEE. |
[20] | Das, S., Islam, M.R., Jayakodi, N.K. and Doppa, J.R., 2019. Active Anomaly Detection via Ensembles: Insights, Algorithms, and Interpretability. arXiv preprint arXiv:1901.08930. |
[21] | Ding, K., Li, J. and Liu, H., 2019, January. Interactive anomaly detection on attributed networks. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pp. 357-365. ACM. |
[22] | Djenouri, Y. and Zimek, A., 2018, June. Outlier detection in urban traffic data. In Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics. ACM. |
[23] | Domingues, R., Filippone, M., Michiardi, P. and Zouaoui, J., 2018. A comparative evaluation of outlier detection algorithms: Experiments and analyses. Pattern Recognition, 74, pp.406-421. |
[24] | Emmott, A., Das, S., Dietterich, T., Fern, A. and Wong, W.K., 2015. A meta-analysis of the anomaly detection problem. arXiv preprint arXiv:1503.01158. |
[25] | Ergen, T. and Kozat, S.S., 2019. Unsupervised Anomaly Detection With LSTM Neural Networks. IEEE transactions on neural networks and learning systems. |
[26] | Falcão, F., Zoppi, T., Silva, C.B.V., Santos, A., Fonseca, B., Ceccarelli, A. and Bondavalli, A., 2019, April. Quantitative comparison of unsupervised anomaly detection algorithms for intrusion detection. In Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, (pp. 318-327). ACM. |
[27] | Garcia-Teodoro, P., Diaz-Verdejo, J., Maciá-Fernández, G. and Vázquez, E., 2009. Anomaly-based network intrusion detection: Techniques, systems and challenges. Computers & Security, 28(1-2), pp.18-28. |
[28] | Goldstein, M. and Uchida, S., 2016. A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data. PloS one, 11(4), p.e0152173. |
[29] | Gupta, M., Gao, J., Aggarwal, C.C. and Han, J., 2014. Outlier detection for temporal data: A survey. IEEE Transactions on Knowledge and Data Engineering, 26(9), pp.2250-2267. |
[30] | Hariri, S., Kind, M.C. and Brunner, R.J., 2019. Extended Isolation Forest. IEEE Transactions on Knowledge and Data Engineering. |
[31] | Hendrycks, D., Mazeika, M. and Dietterich, T.G., 2019. Deep Anomaly Detection with Outlier Exposure. International Conference on Learning Representations (ICLR). |
[32] | Hodge, V. and Austin, J., 2004. A survey of outlier detection methodologies. Artificial intelligence review, 22(2), pp.85-126. |
[33] | (1, 2) Hundman, K., Constantinou, V., Laporte, C., Colwell, I. and Soderstrom, T., 2018, July. Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, (pp. 387-395). ACM. |
[34] | Kannan, R., Woo, H., Aggarwal, C.C. and Park, H., 2017, June. Outlier detection for text data. In Proceedings of the 2017 SIAM International Conference on Data Mining, pp. 489-497. Society for Industrial and Applied Mathematics. |
[35] | Kriegel, H.P., Kröger, P. and Zimek, A., 2010. Outlier detection techniques. Tutorial at ACM SIGKDD 2010. |
[36] | Lamba, H. and Akoglu, L., 2019, May. Learning On-the-Job to Re-rank Anomalies from Top-1 Feedback. In Proceedings of the 2019 SIAM International Conference on Data Mining (SDM), pp. 612-620. Society for Industrial and Applied Mathematics. |
[37] | Lavin, A. and Ahmad, S., 2015, December. Evaluating Real-Time Anomaly Detection Algorithms--The Numenta Anomaly Benchmark. In 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) (pp. 38-44). IEEE. |
[38] | Lazarevic, A., Banerjee, A., Chandola, V., Kumar, V. and Srivastava, J., 2008, September. Data mining for anomaly detection. Tutorial at ECML PKDD 2008. |
[39] | Li, D., Chen, D., Shi, L., Jin, B., Goh, J. and Ng, S.K., 2019. MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks. arXiv preprint arXiv:1901.04997. |
[40] | Li, Z., Zhao, Y., Botta, N., Ionescu, C. and Hu, X. COPOD: Copula-Based Outlier Detection. IEEE International Conference on Data Mining (ICDM), 2020. |
[41] | Liu, F.T., Ting, K.M. and Zhou, Z.H., 2008, December. Isolation forest. In International Conference on Data Mining, pp. 413-422. IEEE. |
[42] | Liu, H., Li, J., Wu, Y. and Fu, Y., 2018. Clustering with Outlier Removal. arXiv preprint arXiv:1801.01899. |
[43] | Liu, N., Shin, D. and Hu, X., 2017. Contextual outlier interpretation. In International Joint Conference on Artificial Intelligence (IJCAI-18), pp.2461-2467. |
[44] | Liu, Y., Li, Z., Zhou, C., Jiang, Y., Sun, J., Wang, M. and He, X., 2019. Generative Adversarial Active Learning for Unsupervised Outlier Detection. IEEE transactions on knowledge and data engineering. |
[45] | Li, Y., Chen, Z., Zha, D., Zhou, K., Jin, H., Chen, H. and Hu, X., 2020. AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning. ICDE. |
[46] | Macha, M. and Akoglu, L., 2018. Explaining anomalies in groups with characterizing subspace rules. Data Mining and Knowledge Discovery, 32(5), pp.1444-1480. |
[47] | Manzoor, E., Lamba, H. and Akoglu, L. Outlier Detection in Feature-Evolving Data Streams. In 24th ACM SIGKDD International Conference on Knowledge Discovery and Data mining (KDD). 2018. |
[48] | Mendiratta, B.V., 2017. Anomaly Detection in Networks. Tutorial at ACM SIGKDD 2017. |
[49] | Micenková, B., McWilliams, B. and Assent, I., 2015. Learning representations for outlier detection on a budget. arXiv preprint arXiv:1507.08104. |
[50] | Gupta, N., Eswaran, D., Shah, N., Akoglu, L. and Faloutsos, C., Beyond Outlier Detection: LookOut for Pictorial Explanation. ECML PKDD 2018. |
[51] | Pang, G., Cao, L., Chen, L. and Liu, H., 2016, December. Unsupervised feature selection for outlier detection by modelling hierarchical value-feature couplings. In Data Mining (ICDM), 2016 IEEE 16th International Conference on (pp. 410-419). IEEE. |
[52] | Pang, G., Cao, L., Chen, L. and Liu, H., 2017, August. Learning homophily couplings from non-iid data for joint feature selection and noise-resilient outlier detection. In Proceedings of the 26th International Joint Conference on Artificial Intelligence (pp. 2585-2591). AAAI Press. |
[53] | (1, 2) Pang, G., Cao, L., Chen, L. and Liu, H., 2018. Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection. In 24th ACM SIGKDD International Conference on Knowledge Discovery and Data mining (KDD). 2018. |
[54] | Pang, G., Shen, C., Cao, L. and Hengel, A.V.D., 2020. Deep Learning for Anomaly Detection: A Review. arXiv preprint arXiv:2007.02500. |
[55] | Pelleg, D. and Moore, A.W., 2005. Active learning for anomaly and rare-category detection. In Advances in neural information processing systems, pp. 1073-1080. |
[56] | Radovanović, M., Nanopoulos, A. and Ivanović, M., 2015. Reverse nearest neighbors in unsupervised distance-based outlier detection. IEEE transactions on knowledge and data engineering, 27(5), pp.1369-1382. |
[57] | Ramaswamy, S., Rastogi, R. and Shim, K., 2000, May. Efficient algorithms for mining outliers from large data sets. ACM SIGMOD Record, 29(2), pp. 427-438. |
[58] | Ranshous, S., Shen, S., Koutra, D., Harenberg, S., Faloutsos, C. and Samatova, N.F., 2015. Anomaly detection in dynamic networks: a survey. Wiley Interdisciplinary Reviews: Computational Statistics, 7(3), pp.223-247. |
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