Anomaly Detection

In this post i'd like to share references and articles which I came across while learning Anomaly Detection Techniques like blogs/ papers / patents/ Wikipedia information etc.

Popular Anomaly Detection Techniques:

• Density-based techniques (k-nearest neighbor, local outlier factor, and many more variations of this concept).

 Knorr, E. M.; Ng, R. T.; Tucakov, V. (2000). "Distance-based outliers: Algorithms and applications". The VLDB Journal the International Journal on Very Large Data Bases 8 (3–4): 237. doi:10.1007/s007780050006.
 Ramaswamy, S.; Rastogi, R.; Shim, K. (2000). Efficient algorithms for mining outliers from large data sets. Proceedings of the 2000 ACM SIGMOD international conference on Management of data - SIGMOD '00. p. 427. doi:10.1145/342009.335437. ISBN 1581132174.
Angiulli, F.; Pizzuti, C. (2002). Fast Outlier Detection in High Dimensional Spaces. Principles of Data Mining and Knowledge Discovery. Lecture Notes in Computer Science 2431. p. 15. doi:10.1007/3-540-45681-3_2. ISBN 978-3-540-44037-6
Breunig, M. M.; Kriegel, H.-P.; Ng, R. T.; Sander, J. (2000). LOF: Identifying Density-based Local Outliers (PDF). Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data. SIGMOD: 93–104. doi:10.1145/335191.335388. ISBN 1-58113-217-4.
Schubert, E.; Zimek, A.; Kriegel, H. -P. (2012). "Local outlier detection reconsidered: A generalized view on locality with applications to spatial, video, and network outlier detection". Data Mining and Knowledge Discovery.

• Subspace and correlation-based outlier detection for high-dimensional data.

Kriegel, H. P.; Kröger, P.; Schubert, E.; Zimek, A. (2009). Outlier Detection in Axis-Parallel Subspaces of High Dimensional Data. Advances in Knowledge Discovery and Data Mining. Lecture Notes in Computer Science 5476. p. 831.
Kroger, P.; Schubert, E.; Zimek, A. (2012). Outlier Detection in Arbitrarily Oriented Subspaces. 2012 IEEE 12th International Conference on Data Mining. p. 379.
 Zimek, A.; Schubert, E.; Kriegel, H.-P. (2012). "A survey on unsupervised outlier detection in high-dimensional numerical data". Statistical Analysis and Data Mining 5 (5): 363–387.

• One class support vector machines

Schölkopf, B.; Platt, J. C.; Shawe-Taylor, J.; Smola, A. J.; Williamson, R. C. (2001). "Estimating the Support of a High-Dimensional Distribution". Neural Computation 13 (7): 1443. 

• Replicator neural networks

• Cluster analysis based outlier detection

He, Z.; Xu, X.; Deng, S. (2003). "Discovering cluster-based local outliers". Pattern Recognition Letters 24 (9–10): 1641.

• Deviations from association rules and frequent itemsets.

• Fuzzy logic based outlier detection.

• Ensemble techniques, using feature bagging, score normalization and different sources of diversity.

Lazarevic, A.; Kumar, V. (2005). "Feature bagging for outlier detection". Proc. 11th ACM SIGKDD international conference on Knowledge Discovery in Data Mining: 157–166.
Nguyen, H. V.; Ang, H. H.; Gopalkrishnan, V. (2010). Mining Outliers with Ensemble of Heterogeneous Detectors on Random Subspaces. Database Systems for Advanced Applications. Lecture Notes in Computer Science 5981. p. 368.
Kriegel, H. P.; Kröger, P.; Schubert, E.; Zimek, A. (2011). Interpreting and Unifying Outlier Scores (PDF). Proceedings of the 2011 SIAM International Conference on Data Mining. pp. 13–24.
Schubert, E.; Wojdanowski, R.; Zimek, A.; Kriegel, H. P. (2012). On Evaluation of Outlier Rankings and Outlier Scores (PDF). Proceedings of the 2012 SIAM International Conference on Data Mining. pp. 1047–1058.
Zimek, A.; Campello, R. J. G. B.; Sander, J. R. (2014). "Ensembles for unsupervised outlier detection". ACM SIGKDD Explorations Newsletter 15: 11.
Zimek, A.; Campello, R. J. G. B.; Sander, J. R. (2014). Data perturbation for outlier detection ensembles. Proceedings of the 26th International Conference on Scientific and Statistical Database Management - SSDBM '14. p. 1