Anomaly detection, also known as outlier detection, is a fascinating and useful technique to identify outlying data objects. It has been proven critical in many fields, such as credit card fraud analytics and mechanical unit defect detection.

In this repository, you will find many:

1. Books & Academic Papers
2. Learning Materials, e.g, online courses and videos
3. Outlier Datasets
4. Outlier Detection Libraries & Demo Codes
5. Paper Downloader: a Python 3 script to download the open access papers listed in this repository.

I would continue adding more items to the repository. Please feel free to suggest some key materials by opening an issue or dropping me an email ([email protected]). Enjoy reading!

• Anomaly Detection Learning Resources
  • 1. Books & Tutorials
    • 1.1. Books
    • 1.2. Tutorials
  • 2. Papers
    • 2.1. Overview & Survey Papers
    • 2.2. Graph & Network Outlier Detection
    • 2.3. Time Series Outlier Detection
    • 2.4. Spatial Outliers
    • 2.5. High-dimensional & Subspace Outliers
    • 2.6. Outlier Ensembles
  • 3. Courses/Seminars/Videos
  • 4. Ouliter Datasets
  • 5. Outlier Detection Libraries and Demo Codes
    • 5.1. Python
    • 5.2. Matlab
    • 5.2. Java
  • 6. Key Conferences/Workshops/Journals
    • 6.1. Conferences & Workshopes
    • 6.2. Journals

Outlier Analysis by Charu Aggarwal: Classical text book covering most of the outlier analysis techniques. A must-read for people in outlier detection. [Preview.pdf]

Outlier Ensembles: An Introduction by Charu Aggarwal and Saket Sathe: Great intro book for ensemble learning in outlier anaysis.

Data Mining: Concepts and Techniques (3rd) by Jiawei Han Micheline Kamber Jian Pei: Chapter 12 discusses outlier detection with many important points. [Google Search]

Kriegel, H.P., Kröger, P. and Zimek, A., 2010. Outlier detection techniques. Tutorial at ACM SIGKDD, 10. [Download PDF]

Chawla, S. and Chandola, V., 2011, Anomaly Detection: A Tutorial. Tutorial at ICDM 2011. [Download PDF]

Lazarevic, A., Banerjee, A., Chandola, V., Kumar, V. and Srivastava, J., 2008, September. Data mining for anomaly detection. In Tutorial at ECML PKDD 2008. [See Video]

Chandola, V., Banerjee, A. and Kumar, V., 2009. Anomaly detection: A survey. ACM computing surveys , 41(3), p.15. [Download PDF]

Hodge, V. and Austin, J., 2004. A survey of outlier detection methodologies. Artificial intelligence review, 22(2), pp.85-126. [Download PDF]

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. [HTML] [SLIDES]

Goldstein, M. and Uchida, S., 2016. A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data. PloS one, 11(4), p.e0152173. [Download PDF]

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. [Download PDF]

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. [Download PDF]

Zimek, A., Schubert, E. and Kriegel, H.P., 2012. A survey on unsupervised outlier detection in high‐dimensional numerical data. Statistical Analysis and Data Mining: The ASA Data Science Journal, 5(5), pp.363-387. [Downloadable Link]

Aggarwal, C.C., 2013. Outlier ensembles: position paper. ACM SIGKDD Explorations Newsletter, 14(2), pp.49-58. [Download PDF]

Zimek, A., Campello, R.J. and Sander, J., 2014. Ensembles for unsupervised outlier detection: challenges and research questions a position paper. ACM Sigkdd Explorations Newsletter, 15(1), pp.11-22. [Download PDF]

Coursera Introduction to Anomaly Detection (by IBM): https://www.coursera.org/learn/ai/lecture/ASPv0/introduction-to-anomaly-detection

Coursera Machine Learning by Andrew Ng also partly covers the topic:

• Anomaly Detection vs. Supervised Learning
• Developing and Evaluating an Anomaly Detection System

Udemy Outlier Detection Algorithms in Data Mining and Data Science: https://www.udemy.com/outlier-detection-techniques/

Stanford Data Mining for Cyber Security also covers part of anomaly detection techniques. http://web.stanford.edu/class/cs259d/

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

Scikit-learn Novelty and Outlier Detection. It supports some popular algorithms like LOF, Isolation Forest and One-class SVM

Python Outlier Detection (PyOD): Under construction, it supports a series of outlier detection algorithms and combination frameworks

Anomaly Detection Toolbox - Beta: A collection of popular outlier detection algorithms in Matlab.

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.

ACM SIGKDD Conference on Knowledge Discovery and Data Mining (http://www.kdd.org/conferences) ACM SIGMOD IEEE ICDM IEEE ICDE ACM CIKM ACM WSDM

TKDD TKDE