Awesome Outlier Detection Resources

An awesome curated list of outlier (a.k.a anomaly) detection papers.

Outlier Detection

In data mining, anomaly detection (also outlier detection) is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. - Wikipedia

Table of Contents

• 1. Books
• 2. Research Papers
  • 2.1. Survey Papers
  • 2.2. State-of-the-Art Papers
  • 2.3. Density Based Outlier Detection Methods
  • 2.4. Distance Based Outlier Detection Methods
  • 2.5. Clustering Based Outlier Detection Methods
  • 2.6. Isolation Based Outlier Detection Methods
  • 2.7. Subspace Outlier Detection Methods
  • 2.8. Ensemble based Outlier Detection Methods
  • 2.9. Deep Learning Outlier Detection Methods
  • 2.10. Graph Outlier Detection
  • 2.11. Outlying Aspect Mining
• 3. Tutorials
• 4. Datasets
• 5. Tools

1. Books

Outlier Analysis by Charu C. Aggarwal [URL].

2. Research Papers

2.1. Survey Papers

Title	Publication Venue	Year	Reference	URL
Novelty detection: a review—part 1: statistical approaches	Elsevier	2003	[1]	[URL]
Novelty detection: a review—part 2:: neural network based approaches	Elsevier	2003	[2]	[URL]
A Survey of Outlier Detection Methodologies	Springer	2004	[3]	[URL] [PDF]
Anomaly detection: A survey	ACM	2009	[4]	[PDF]
A Comprehensive Survey of Data Mining-based Fraud Detection Research	ArXiv Preprint	2010	[15]	[PDF]
A survey on unsupervised outlier detection in high‐dimensional numerical data	Wiley Online Library	2012	[16]	[URL]
Survey on Anomaly Detection using Data Mining Techniques	ScienceDirect	2015	[14]	[URL]
Graph based anomaly detection and description: a survey	DMKD	2015	[47]	[URL] [PDF]
A comparative evaluation of outlier detection algorithms: Experiments and analyses	Pattern Recognition	2018	[48]	[PDF]
Progress in outlier detection techniques: A survey	IEEE Access	2019	[46]	[URL]
Deep learning for anomaly detection: A survey	ArXiv	2019	[49]	[PDF]
Deep learning for anomaly detection: A review	ArXiv	2020	[50]	[PDF]

2.2. State-of-the-Art Papers

Title	Publication Venue	Year	Reference	URL
LOF: Identifying Density-Based Local Outliers	ACM SIGMOD Record	2000	[6]	[PDF]
Efficient algorithms for mining outliers from large data sets	ACM SIGMOD Record	2000	[17]	[PDF]
Fast outlier detection in high dimensional spaces	PKDD	2002	[33]	[PDF]
Isolation Forest	IEEE	2008	[5]	[URL]

2.3. Density Based Outlier Detection Methods

Title	Publication Venue	Year	Reference	URL
OPTICS-OF: Identifying Local Outliers	Springer	1999	[9]	[URL]
LOF: Identifying Density-Based Local Outliers	ACM SIGMOD Record	2000	[6]	[PDF]
Enhancing effectiveness of outlier detections for low density patterns (COF)	PAKDD	2002	[55]	[URL]
RDF: A density-based outlier detection method using vertical data representation	ICDM	2004	[57]	[URL]
LOCI: fast outlier detection using the local correlation integral	IEEE	2003	[7]	[URL]
LoOP: local outlier probabilities	CIKM	2009	[58]	[PDF]
Resolution-based outlier factor: detecting the top-n most outlying data points in engineering data (ROF)	KAIS	2009	[59]	[URL]
FastLOF: An expectation-maximization based local outlier detection algorithm	ICPR	2012	[60]	[URL]
Local outlier detection reconsidered: a generalized view on locality with applications to spatial, video, and network outlier detection (SimplifiedLOF)	DMKD	2014	[56]	[URL]
LiNearN: A new approach to nearest neighbour density estimator	Pattern Recognition	2014	[52]	[URL]
Revisiting Attribute Independence Assumption in Probabilistic Unsupervised Anomaly Detection	Springer	2016	[8]	[URL]
Hierarchical density estimates for data clustering, visualization, and outlier detection (GLOSH)	TKDD	2015	[61]	[URL]
Improved histogram-based anomaly detector with the extended principal component features	arXiv preprint	2019	[51]	[PDF]

2.4. Distance Based Outlier Detection Methods

Title	Publication Venue	Year	Reference	URL
Efficient algorithms for mining outliers from large data sets	ACM SIGMOD Record	2000	[17]	[PDF]
Fast outlier detection in high dimensional spaces	PKDD	2002	[33]	[PDF]
A New Local Distance-Based Outlier Detection Approach for Scattered Real-World Data	PAKDD	2009	[30]	[URL]
Rapid Distance-Based Outlier Detection via Sampling	NIPS	2013	[32]	[PDF]
Distance-based Outlier Detection in Data Streams	VLDB	2016	[31]	[PDF]

2.5. Clustering Based Outlier Detection Methods

Title	Publication	Year	Reference	URL
Clustering-Based Outlier Detection Method	FSKD	2008	[35]	[URL]
Efficient Clustering-Based Outlier Detection Algorithm for Dynamic Data Stream	FSKD	2008	[36]	[URL]
Cluster-based outlier detection	Annals of Operations Research	2009	[34]	[PDF]
Framework of Clustering-Based Outlier Detection	FSKD	2009	[40]	[URL]
An Outlier Detection Method Based on Clustering	EAIT	2011	[37]	[PDF]
A Minimum Spanning Tree-Inspired Clustering-Based Outlier Detection Technique	ICDM	2012	[39]	[PDF]
Cluster Based Outlier Detection Algorithm for Healthcare Data	Procedia Computer Science	2015	[38]	[PDF]

2.6. Isolation Based Outlier Detection Methods

Title	Publication Venue	Year	Reference	URL
Isolation Forest	IEEE	2008	[5]	[URL]
On Detecting Clustered Anomalies Using SCiForest	Springer	2010	[12]	[PDF]
Isolation-Based Anomaly Detection	ACM	2012	[10]	[PDF]
Improving iForest with Relative Mass	Springer	2014	[11]	[URL]
Efficient anomaly detection by isolation using nearest neighbour ensemble	ICDEW	2014	[42]	[URL]
LeSiNN: Detecting anomalies by identifying Least Similar Nearest Neighbours	IEEE	2015	[13]	[URL]
Isolation‐based anomaly detection using nearest‐neighbor ensembles	Computational Intelligence	2018	[45]	[URL]
Anomaly Detection Technique Robust to Units and Scales of Measurement	PAKDD	2018	[53]	[URL]
usfAD: a robust anomaly detector based on unsupervised stochastic forest	International Journal of Machine Learning and Cybernetics	2020	[54]	[URL]

2.7. Subspace Outlier Detection Methods

Title	Publication Venue	Year	Reference	URL
Outlier Detection in Axis-Parallel Subspaces of High Dimensional Data	Springer	2009	[28]	[URL] [PDF]
Local Subspace Based Outlier Detection	Springer	2009	[24]	[URL]
HiCS: High Contrast Subspaces for Density-Based Outlier Ranking	ACM	2012	[21]	[URL]
Outlier Detection in Arbitrarily Oriented Subspaces	ICDM	2012	[26]	[PDF]
An angle-based subspace anomaly detection approach to high-dimensional data: With an application to industrial fault detection	Elsevier	2015	[27]	[URL] [PDF]
ZERO++: Harnessing the Power of Zero Appearances to Detect Anomalies in Large-Scale Data Sets	JAIR	2016	[23]	[URL] [PDF]
Subspace Outlier Detection in Linear Time with Randomized Hashing	IEEE	2016	[25]	[URL] [PDF]
Hiding outliers in high-dimensional data spaces	Springer	2017	[22]	[URL]

2.8. Ensemble based Outlier Detection Methods

Title	Publication	Year	Reference	URL
LODA: Lightweight on-line detector of anomalies	Machine Learning	2016	[62]	[PDF]
LSCP: Locally selective combination in parallel outlier ensembles	SIAM	2019	[63]	[PDF]
DCSO: dynamic combination of detector scores for outlier ensemble	ArXiv	2019	[64]	[PDF]

2.9. Deep Learning Outlier Detection Methods

Title	Publication	Year	Reference	URL
DeepLog: Anomaly Detection and Diagnosis from System Logs through Deep Learning	CCS conference	2017	[65]	[PDF]

2.10. Graph Outlier Detection

Title	Publication	Year	Reference	URL
Graph based anomaly detection and description: a survey	DMKD	2015	[67]	[PDF]
On Using Classification Datasets to Evaluate Graph Outlier Detection: Peculiar Observations and New Insights	ArXiv	2020	[66]	[PDF]

2.11. Outlying Aspect Mining

Title	Publication Venue	Year	Reference	URL
Hos-Miner: a system for detecting outlyting subspaces of high-dimensional data	VLDB	2004	[20]	[PDF]
Mining outlying aspects on numeric data	Springer	2015	[19]	[URL]
Discovering outlying aspects in large datasets	Springer	2016	[18]	[PDF]
Scalable Outlying-Inlying Aspects Discovery via Feature Ranking	PAKDD	2015	[29]	[URL]
A new effective and efficient measure for outlying aspect mining	WISE	2020	[41]	[URL]

3. Tutorials

Title	Publication	Year	Reference	URL
Outlier detection techniques	KDD	2010	[44]	[URL]
Which Outlier Detector Should I use?	ICDE	2018	[43]	[URL]

4. Datasets

ODDS - Outlier Detection DataSets

5. Tools

Tool	Language	URL
ELKI	Java	[URL]
PyOD	Python	[URL]

References

[1]	Markou, M., & Singh, S. (2003). Novelty detection: a review—part 1: statistical approaches. Signal processing, 83(12), 2481-2497.
[2]	Markou, M., & Singh, S. (2003). Novelty detection: a review—part 2:: neural network based approaches. Signal processing, 83(12), 2499-2521.
[3]	Hodge, V., & Austin, J. (2004). A survey of outlier detection methodologies. Artificial intelligence review, 22(2), 85-126.
[4]	Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM computing surveys (CSUR), 41(3), 1-58.
[5]	Liu, F. T., Ting, K. M., & Zhou, Z. H. (2008, December). Isolation forest. In 2008 Eighth IEEE International Conference on Data Mining (pp. 413-422). IEEE.
[6]	Breunig, M. M., Kriegel, H. P., Ng, R. T., & Sander, J. (2000, May). LOF: identifying density-based local outliers. In Proceedings of the 2000 ACM SIGMOD international conference on Management of data (pp. 93-104).
[7]	Papadimitriou, S., Kitagawa, H., Gibbons, P. B., & Faloutsos, C. (2003, March). Loci: Fast outlier detection using the local correlation integral. In Proceedings 19th international conference on data engineering (Cat. No. 03CH37405) (pp. 315-326). IEEE.
[8]	Aryal, S., Ting, K. M., & Haffari, G. (2016, April). Revisiting attribute independence assumption in probabilistic unsupervised anomaly detection. In Pacific-Asia Workshop on Intelligence and Security Informatics (pp. 73-86). Springer, Cham.
[9]	Breunig, M. M., Kriegel, H. P., Ng, R. T., & Sander, J. (1999, September). Optics-of: Identifying local outliers. In European Conference on Principles of Data Mining and Knowledge Discovery (pp. 262-270). Springer, Berlin, Heidelberg.
[10]	Liu, F. T., Ting, K. M., & Zhou, Z. H. (2012). Isolation-based anomaly detection. ACM Transactions on Knowledge Discovery from Data (TKDD), 6(1), 1-39.
[11]	Aryal, S., Ting, K. M., Wells, J. R., & Washio, T. (2014, May). Improving iforest with relative mass. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 510-521). Springer, Cham.
[12]	Liu, F. T., Ting, K. M., & Zhou, Z. H. (2010, September). On detecting clustered anomalies using SCiForest. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 274-290). Springer, Berlin, Heidelberg.
[13]	Pang, G., Ting, K. M., & Albrecht, D. (2015, November). LeSiNN: Detecting anomalies by identifying least similar nearest neighbours. In 2015 IEEE international conference on data mining workshop (ICDMW) (pp. 623-630). IEEE.
[14]	Agrawal, S., & Agrawal, J. (2015). Survey on anomaly detection using data mining techniques. Procedia Computer Science, 60, 708-713.
[15]	Phua, C., Lee, V., Smith, K., & Gayler, R. (2010). A comprehensive survey of data mining-based fraud detection research. arXiv preprint arXiv:1009.6119.
[16]	Zimek, A., Schubert, E., & 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), 363-387.
[17]	Ramaswamy, S., Rastogi, R., & Shim, K. (2000, May). Efficient algorithms for mining outliers from large data sets. In Proceedings of the 2000 ACM SIGMOD international conference on Management of data (pp. 427-438).
[18]	Vinh, N. X., Chan, J., Romano, S., Bailey, J., Leckie, C., Ramamohanarao, K., & Pei, J. (2016). Discovering outlying aspects in large datasets. Data mining and knowledge discovery, 30(6), 1520-1555.
[19]	Duan, L., Tang, G., Pei, J., Bailey, J., Campbell, A., & Tang, C. (2015). Mining outlying aspects on numeric data. Data Mining and Knowledge Discovery, 29(5), 1116-1151.
[20]	Zhang, J., Lou, M., Ling, T. W., & Wang, H. (2004). HOS-miner: A system for detecting outlying subspaces of high-dimensional data. In Proceedings of the 30th International Conference on Very Large Data Bases (VLDB'04) (pp. 1265-1268). Morgan Kaufmann Publishers Inc..
[21]	Keller, F., Muller, E., & Bohm, K. (2012, April). HiCS: High contrast subspaces for density-based outlier ranking. In 2012 IEEE 28th international conference on data engineering (pp. 1037-1048). IEEE.
[22]	Steinbuss, G., & Böhm, K. (2017). Hiding outliers in high-dimensional data spaces. International Journal of Data Science and Analytics, 4(3), 173-189.
[23]	Pang, G., Ting, K. M., Albrecht, D., & Jin, H. (2016). ZERO++: Harnessing the power of zero appearances to detect anomalies in large-scale data sets. Journal of Artificial Intelligence Research, 57, 593-620.
[24]	Agrawal, A. (2009, August). Local subspace based outlier detection. In International Conference on Contemporary Computing (pp. 149-157). Springer, Berlin, Heidelberg.
[25]	Sathe, S., & Aggarwal, C. C. (2016, December). Subspace outlier detection in linear time with randomized hashing. In 2016 IEEE 16th International Conference on Data Mining (ICDM) (pp. 459-468). IEEE.
[26]	Kriegel, H. P., Kröger, P., Schubert, E., & Zimek, A. (2012, December). Outlier detection in arbitrarily oriented subspaces. In 2012 IEEE 12th international conference on data mining (pp. 379-388). IEEE.
[27]	Zhang, L., Lin, J., & Karim, R. (2015). An angle-based subspace anomaly detection approach to high-dimensional data: With an application to industrial fault detection. Reliability Engineering & System Safety, 142, 482-497.
[28]	Kriegel, H. P., Kröger, P., Schubert, E., & Zimek, A. (2009, April). Outlier detection in axis-parallel subspaces of high dimensional data. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 831-838). Springer, Berlin, Heidelberg.
[29]	Vinh, N. X., Chan, J., Bailey, J., Leckie, C., Ramamohanarao, K., & Pei, J. (2015, May). Scalable outlying-inlying aspects discovery via feature ranking. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 422-434). Springer, Cham.
[30]	Zhang, K., Hutter, M., & Jin, H. (2009, April). A new local distance-based outlier detection approach for scattered real-world data. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 813-822). Springer, Berlin, Heidelberg.
[31]	Tran, L., Fan, L., & Shahabi, C. (2016). Distance-based outlier detection in data streams. Proceedings of the VLDB Endowment, 9(12), 1089-1100.
[32]	Sugiyama, M., & Borgwardt, K. (2013). Rapid distance-based outlier detection via sampling. In Advances in Neural Information Processing Systems (pp. 467-475).
[33]	Angiulli, F., & 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). Springer, Berlin, Heidelberg.
[34]	Duan, L., Xu, L., Liu, Y., & Lee, J. (2009). Cluster-based outlier detection. Annals of Operations Research, 168(1), 151-168.
[35]	Jiang, S. Y., & An, Q. B. (2008, October). Clustering-based outlier detection method. In 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery (Vol. 2, pp. 429-433). IEEE.
[36]	Elahi, M., Li, K., Nisar, W., Lv, X., & Wang, H. (2008, October). Efficient clustering-based outlier detection algorithm for dynamic data stream. In 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery (Vol. 5, pp. 298-304). IEEE.
[37]	Pamula, R., Deka, J. K., & Nandi, S. (2011, February). An outlier detection method based on clustering. In 2011 Second International Conference on Emerging Applications of Information Technology (pp. 253-256). IEEE.
[38]	Christy, A., Gandhi, G. M., & Vaithyasubramanian, S. (2015). Cluster based outlier detection algorithm for healthcare data. Procedia Computer Science, 50, 209-215.
[39]	Wang, X., Wang, X. L., & Wilkes, D. M. (2012, July). A minimum spanning tree-inspired clustering-based outlier detection technique. In Industrial Conference on Data Mining (pp. 209-223). Springer, Berlin, Heidelberg.
[40]	Jiang, S. Y., & Yang, A. M. (2009, August). Framework of clustering-based outlier detection. In 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery (Vol. 1, pp. 475-479). IEEE.
[41]	Samariya, D., Aryal, S., Ting, K. M., & Ma, J. (2020, October). A new effective and efficient measure for outlying aspect mining. In International Conference on Web Information Systems Engineering (pp. 463-474). Springer, Cham.
[42]	T. R. Bandaragoda, K. M. Ting, D. Albrecht, F. T. Liu and J. R. Wells, "Efficient Anomaly Detection by Isolation Using Nearest Neighbour Ensemble," 2014 IEEE International Conference on Data Mining Workshop, Shenzhen, 2014, pp. 698-705, doi: 10.1109/ICDMW.2014.70.
[43]	Ting, K. M., Aryal, S., & Washio, T. (2018, November). Which Outlier Detector Should I use?. In 2018 IEEE International Conference on Data Mining (ICDM) (pp. 8-8). IEEE.
[44]	Kriegel, H. P., Kröger, P., & Zimek, A. (2010). Outlier detection techniques. Tutorial at KDD, 10, 1-76.
[45]	Bandaragoda, T. R., Ting, K. M., Albrecht, D., Liu, F. T., Zhu, Y., & Wells, J. R. (2018). Isolation‐based anomaly detection using nearest‐neighbor ensembles. Computational Intelligence, 34(4), 968-998.
[46]	Wang, H., Bah, M. J., & Hammad, M. (2019). Progress in outlier detection techniques: A survey. IEEE Access, 7, 107964-108000.
[47]	Akoglu, L., Tong, H., & Koutra, D. (2015). Graph based anomaly detection and description: a survey. Data mining and knowledge discovery, 29(3), 626-688.
[48]	Domingues, R., Filippone, M., Michiardi, P., & Zouaoui, J. (2018). A comparative evaluation of outlier detection algorithms: Experiments and analyses. Pattern Recognition, 74, 406-421.
[49]	Chalapathy, R., & Chawla, S. (2019). Deep learning for anomaly detection: A survey. arXiv preprint arXiv:1901.03407.
[50]	Pang, G., Shen, C., Cao, L., & Hengel, A. V. D. (2020). Deep learning for anomaly detection: A review. arXiv preprint arXiv:2007.02500.
[51]	Aryal, S., Baniya, A. A., & Santosh, K. C. (2019). Improved histogram-based anomaly detector with the extended principal component features. arXiv preprint arXiv:1909.12702.
[52]	Wells, J. R., Ting, K. M., & Washio, T. (2014). LiNearN: A new approach to nearest neighbour density estimator. Pattern Recognition, 47(8), 2702-2720.
[53]	Aryal, S. (2018, June). Anomaly detection technique robust to units and scales of measurement. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 589-601). Springer, Cham.
[54]	Aryal, S., Santosh, K. C., & Dazeley, R. (2020). usfAD: a robust anomaly detector based on unsupervised stochastic forest. International Journal of Machine Learning and Cybernetics, 1-14.
[55]	Tang, J., Chen, Z., Fu, A. W. C., & Cheung, D. W. (2002, May). Enhancing effectiveness of outlier detections for low density patterns. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 535-548). Springer, Berlin, Heidelberg.
[56]	Schubert, E., Zimek, A., & Kriegel, H. P. (2014). Local outlier detection reconsidered: a generalized view on locality with applications to spatial, video, and network outlier detection. Data mining and knowledge discovery, 28(1), 190-237.
[57]	Ren, D., Wang, B., & Perrizo, W. (2004, November). Rdf: A density-based outlier detection method using vertical data representation. In Fourth IEEE International Conference on Data Mining (ICDM'04) (pp. 503-506). IEEE.
[58]	Kriegel, H. P., Kröger, P., Schubert, E., & Zimek, A. (2009, November). LoOP: local outlier probabilities. In Proceedings of the 18th ACM conference on Information and knowledge management (pp. 1649-1652).
[59]	Fan, H., Zaïane, O. R., Foss, A., & Wu, J. (2009). Resolution-based outlier factor: detecting the top-n most outlying data points in engineering data. Knowledge and Information Systems, 19(1), 31-51.
[60]	Goldstein, M. (2012, November). FastLOF: An expectation-maximization based local outlier detection algorithm. In Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012) (pp. 2282-2285). IEEE.
[61]	Campello, R. J., Moulavi, D., Zimek, A., & Sander, J. (2015). Hierarchical density estimates for data clustering, visualization, and outlier detection. ACM Transactions on Knowledge Discovery from Data (TKDD), 10(1), 1-51.
[62]	Pevný, T. (2016). Loda: Lightweight on-line detector of anomalies. Machine Learning, 102(2), 275-304.
[63]	Zhao, Y., Nasrullah, Z., Hryniewicki, M. K., & Li, Z. (2019, May). LSCP: Locally selective combination in parallel outlier ensembles. In Proceedings of the 2019 SIAM International Conference on Data Mining (pp. 585-593). Society for Industrial and Applied Mathematics.
[64]	Zhao, Y., & Hryniewicki, M. K. (2019). DCSO: dynamic combination of detector scores for outlier ensembles. arXiv preprint arXiv:1911.10418.
[65]	Du, M., Li, F., Zheng, G., & Srikumar, V. (2017, October). Deeplog: Anomaly detection and diagnosis from system logs through deep learning. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (pp. 1285-1298).
[66]	Zhao, L., & Akoglu, L. (2020). On Using Classification Datasets to Evaluate Graph Outlier Detection: Peculiar Observations and New Insights. arXiv preprint arXiv:2012.12931.
[67]	Akoglu, L., Tong, H., & Koutra, D. (2015). Graph based anomaly detection and description: a survey. Data mining and knowledge discovery, 29(3), 626-688.

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More to come...

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 @ (samariya.durgesh@gmail.com). Enjoy reading!

Last updated on January 2, 2021