A Survey of Outlier Detection Methodologies

Induction of decision trees

Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439 · see citing works

Self-Organizing Maps

Kohonen, T. (1995) · Springer series in information sciences · doi:10.1007/978-3-642-56927-2 · Cited by 16,032 · see citing works

Neural Networks for Pattern Recognition

Bishop, C. M. (1995) · doi:10.1093/oso/9780198538493.001.0001 · Cited by 12,287 · see citing works

Classification and regression trees

Praagman, J. (1985) · European Journal of Operational Research · doi:10.1016/0377-2217(85)90321-2 · Cited by 10,053 · see citing works

Proceedings of the 25th international conference on Machine learning

Cohen, W. W., McCallum, A., & Roweis, S. T. (2008) · Cited by 5,691 · see citing works

Procedures for Detecting Outlying Observations in Samples

Grubbs, F. E. (1969) · Technometrics · doi:10.1080/00401706.1969.10490657 · Cited by 4,105 · see citing works

BIRCH: an efficient data clustering method for very large databases

Zhang, T., Ramakrishnan, R., & Livny, M. (1996) · Proceedings of the 1996 ACM SIGMOD international conference on Management of data - SIGMOD '96 · doi:10.1145/233269.233324 · Cited by 3,935 · see citing works

Fast Effective Rule Induction

Cohen, W. W. (1995) · Elsevier eBooks · doi:10.1016/b978-1-55860-377-6.50023-2 · Cited by 3,890 · see citing works

A massively parallel architecture for a self-organizing neural pattern recognition machine

Carpenter, G. A., & Grossberg, S. (1987) · Computer Vision, Graphics, and Image Processing · doi:10.1016/s0734-189x(87)80014-2 · Cited by 2,525 · see citing works

Efficient and Effective Clustering Methods for Spatial Data Mining

Ng, R. T., & Han, J. (1994) · Cited by 1,789 · see citing works

Proceedings of the Second International Conference on Knowledge Discovery and Data Mining

Simoudis, E., Han, J., & Fayyad, U. M. (1996) · Cited by 1,121 · see citing works

Outlier detection for high dimensional data

Aggarwal, C. C., & Yu, P. S. (2001) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/375663.375668 · Cited by 1,066 · see citing works

Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining

(2001) · doi:10.1145/502512 · Cited by 433 · see citing works

Novelty detection and neural network validation

Bishop, C. M. (1994) · IEE Proceedings - Vision Image and Signal Processing · doi:10.1049/ip-vis:19941330 · Cited by 395 · see citing works

Neural Computing: An Introduction

Beale, R., & Jackson, T. (1990) · Institute of Physics eBooks · doi:10.1887/0852742622 · Cited by 344 · see citing works

Nearest-Neighbor Clutter Removal for Estimating Features in Spatial Point Processes

Byers, S., & Raftery, A. E. (1998) · Journal of the American Statistical Association · doi:10.1080/01621459.1998.10473711 · Cited by 299 · see citing works

Machine learning: An artificial intelligence approach

VanLehn, K. A. (1985) · Artificial Intelligence · doi:10.1016/0004-3702(85)90004-9 · Cited by 265 · see citing works

Proceedings 2003 VLDB Conference

(2003) · Elsevier eBooks · doi:10.1016/b978-0-12-722442-8.x5000-9 · Cited by 253 · see citing works

Informal identification of outliers in medical data

Laurikkala, J. P. S., Juhola, M., & Kentala, E. L. (2000) · Cited by 220 · see citing works

Proceedings of the European Symposium on Artificial Neural Networks

Tang, T. B., & Murray, A. T. (2006) · Cited by 202 · see citing works

Proceedings of the First International Conference on Knowledge Discovery and Data Mining

Fayyad, U. M., & Uthurusamy, R. (1995) · Knowledge Discovery and Data Mining · Cited by 193 · see citing works

Novelty detection using extreme value statistics

Roberts, S. (1999) · IEE Proceedings - Vision Image and Signal Processing · doi:10.1049/ip-vis:19990428 · Cited by 190 · see citing works

A Probabilistic Resource Allocating Network for Novelty Detection

Roberts, S., & Tarassenko, L. (1994) · Neural Computation · doi:10.1162/neco.1994.6.2.270 · Cited by 187 · see citing works

Statistical Independence and Novelty Detection with Information Preserving Nonlinear Maps

Parra, L. C., Deco, G., & Miesbach, S. (1996) · Neural Computation · doi:10.1162/neco.1996.8.2.260 · Cited by 141 · see citing works

Feature Selection for Case-Based Classification of Cloud Types: An Empirical Comparison

Aha, D. W., & Bankert, R. L. (1994) · Cited by 130 · see citing works

A study of distance-based machine learning algorithms

Dietterich, T. G., & Wettschereck, D. (1994) · Cited by 124 · see citing works

A System for the Analysis of Jet Engine Vibration Data

Nairac, A., Townsend, N. W., Carr, R., et al. (1999) · Integrated Computer-Aided Engineering · doi:10.3233/ica-1999-6106 · Cited by 91 · see citing works

Markov monitoring with unknown states

Smyth, P. (1994) · IEEE Journal on Selected Areas in Communications · doi:10.1109/49.339929 · Cited by 86 · see citing works

Effectiveness of feature extraction in neural network architectures for novelty detection

Addison, J. F. D. (1999) · doi:10.1049/cp:19991239 · Cited by 21 · see citing works

Applications of artificial intelligence in engineering X

Adey, R. A., Rzevski, G., & Tasso, C. (1995) · Cited by 13 · see citing works

Novelty detection in airframe strain data

Hickinbotham, S., & Austin, J. (2002) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2000.906130 · Cited by 8 · see citing works

Anomaly detection: A survey

Chandola, V., Banerjee, A., & Kumar, V. (2009) · ACM Computing Surveys · doi:10.1145/1541880.1541882 · Cited by 11,687 · see citing works

Deep Learning for Anomaly Detection: A Review

Pang, G., Shen, C., Cao, L., & Hengel, A. V. D. (2022) · ACM Computing Surveys · doi:10.1145/3439950 · Cited by 2,837 · see citing works

Data-driven Soft Sensors in the process industry

Kadlec, P., Gabrys, B., & Strandt, S. (2009) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2008.12.012 · Cited by 1,845 · see citing works

A review of novelty detection

Pimentel, M. A. F., Clifton, D. A., Clifton, L., & Tarassenko, L. (2014) · Signal Processing · doi:10.1016/j.sigpro.2013.12.026 · Cited by 1,649 · see citing works

Machine learning: a review of classification and combining techniques

Kotsiantis, S. B., Zaharakis, I. D., & Pintelas, P. E. (2006) · Artificial Intelligence Review · doi:10.1007/s10462-007-9052-3 · Cited by 1,582 · see citing works

A Review on Outlier/Anomaly Detection in Time Series Data

Blázquez-García, A., Conde, A., Mori, U., & Lozano, J. A. (2022) · ACM Computing Surveys · doi:10.1145/3444690 · Cited by 1,102 · see citing works

An overview of online fake news: Characterization, detection, and discussion

Zhang, X., & Ghorbani, A. A. (2020) · Information Processing & Management · doi:10.1016/j.ipm.2019.03.004 · Cited by 997 · see citing works

Machine learning for internet of things data analysis: a survey

Mahdavinejad, M. S., Rezvan, M., Barekatain, M., Adibi, P., Barnaghi, P., & Sheth, A. P. (2018) · Digital Communications and Networks · doi:10.1016/j.dcan.2017.10.002 · Cited by 921 · see citing works

A survey on unsupervised outlier detection in high‐dimensional numerical data

Zimek, A., Schubert, E., & Kriegel, H. (2012) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.11161 · Cited by 864 · see citing works

Machine learning in wireless sensor networks: Algorithms, strategies, and applications

Alsheikh, M. A., Lin, S., Niyato, D., & Tan, H.-P. (2014) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2014.2320099 · Cited by 965 · see citing works

“Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI

Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P., & Aroyo, L. M. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445518 · Cited by 781 · see citing works

Generalized Out-of-Distribution Detection: A Survey

Yang, J., Zhou, K., Li, Y., & Liu, Z. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02117-4 · Cited by 884 · see citing works

Attack of the tails: Yes, you really can backdoor federated learning

Wang, H., Sreenivasan, K., Rajput, S., Vishwakarma, H., Agarwal, S., Sohn, J.-Y., Lee, K., & Papailiopoulos, D. (2020) · arXiv · doi:10.48550/arxiv.2007.05084 · Cited by 406 · see citing works

Outlier Detection: Methods, Models, and Classification

Boukerche, A., Zheng, L., & Alfandi, O. (2021) · ACM Computing Surveys · doi:10.1145/3381028 · Cited by 334 · see citing works

Recent advances in sensor fault diagnosis: a review

Li, D., Wang, Y., Wang, J., Wang, C., & Duan, Y. (2020) · Sensors and Actuators A Physical · doi:10.1016/j.sna.2020.111990 · Cited by 323 · see citing works

Tutorial on practical tips of the most influential data preprocessing algorithms in data mining

García, S., Luengo, J., & Herrera, F. (2016) · Knowledge-Based Systems · doi:10.1016/j.knosys.2015.12.006 · Cited by 310 · see citing works

A systematic survey of computer-aided diagnosis in medicine: Past and present developments

Yanase, J., & Triantaphyllou, E. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.112821 · Cited by 303 · see citing works

Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing

Kohavi, R., Tang, D., & Xu, Y. (2020) · doi:10.1017/9781108653985 · Cited by 297 · see citing works

Association of a germline copy number polymorphism of APOBEC3A and APOBEC3B with burden of putative APOBEC-dependent mutations in breast cancer

Nik-Zainal, S., Wedge, D. C., Alexandrov, L. B., Petljak, M., Butler, A. P., Bolli, N., Davies, H. R., Knappskog, S., Martin, S., Papaemmanuil, E., et al. (2014) · Nature Genetics · doi:10.1038/ng.2955 · Cited by 277 · see citing works

A comprehensive survey of numeric and symbolic outlier mining techniques

Agyemang, M., Barker, K., & Alhajj, R. (2006) · Intelligent Data Analysis · doi:10.3233/ida-2006-10604 · Cited by 164 · see citing works

A Survey on Explainable Anomaly Detection

Li, Z., Zhu, Y., & Van Leeuwen, M. (2024) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3609333 · Cited by 139 · see citing works

An incorrect data detection method for big data cleaning of machinery condition monitoring

Xu, X., Lei, Y., & Li, Z. (2020) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2019.2903774 · Cited by 134 · see citing works

Machine Learning-Enabled Internet of Things (IoT): Data, Applications, and Industry Perspective

Bzai, J., Alam, F., Dhafer, A., Bojović, M., Altowaijri, S. M., Niazi, I. K., & Mehmood, R. (2022) · Electronics · doi:10.3390/electronics11172676 · Cited by 110 · see citing works

Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection

Akcay, S., Atapour-Abarghouei, A., & Breckon, T. P. (2019) · 2019 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8851808 · Cited by 103 · see citing works

Semi-Supervised Anomaly Detection for EEG Waveforms Using Deep Belief Nets

Wulsin, D., Blanco, J., Mani, R., & Litt, B. (2010) · 2010 Ninth International Conference on Machine Learning and Applications · doi:10.1109/icmla.2010.71 · Cited by 98 · see citing works

Detecting outlying properties of exceptional objects

Angiulli, F., Fassetti, F., & Palopoli, L. (2009) · ACM Transactions on Database Systems · doi:10.1145/1508857.1508864 · Cited by 88 · see citing works

Open Category Classification by Adversarial Sample Generation

Yu, Y., Qu, W.-Y., Li, N., & Guo, Z. (2017) · Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/469 · Cited by 84 · see citing works

Rule extraction in unsupervised anomaly detection for model explainability: Application to OneClass SVM

Barbado, A., Corcho, Ó., & Benjamins, R. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.116100 · Cited by 77 · see citing works

A survey on outlier explanations

Panjei, E., Gruenwald, L., Leal, E., Nguyen, C., & Silvia, S. (2022) · The VLDB Journal · doi:10.1007/s00778-021-00721-1 · Cited by 67 · see citing works

A decomposition of the outlier detection problem into a set of supervised learning problems

Paulheim, H., & Meusel, R. (2015) · Machine Learning · doi:10.1007/s10994-015-5507-y · Cited by 62 · see citing works

Artificial intelligence and machine learning in glass science and technology: 21 challenges for the 21 ^st century

Ravinder, Venugopal, V., Bishnoi, S., Singh, S., Zaki, M., Grover, H. S., Bauchy, M., Agarwal, M., & Krishnan, N. M. A. (2021) · International Journal of Applied Glass Science · doi:10.1111/ijag.15881 · Cited by 56 · see citing works

Hunting for Insider Threats Using LSTM-Based Anomaly Detection

Villarreal-Vasquez, M., Modelo-Howard, G., Dube, S., & Bhargava, B. (2023) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2021.3135639 · Cited by 52 · see citing works

Machine learning for anomaly detection in cyanobacterial fluorescence signals

Almuhtaram, H., Zamyadi, A., & Hofmann, R. (2021) · Water Research · doi:10.1016/j.watres.2021.117073 · Cited by 49 · see citing works

Anomaly Detection in a Large-Scale Cloud Platform

Islam, M. S., Pourmajidi, W., Zhang, L., Steinbacher, J., Erwin, T., & Miranskyy, A. (2021) · 2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) · doi:10.1109/icse-seip52600.2021.00024 · Cited by 54 · see citing works

SNIPER: Few-shot Learning for Anomaly Detection to Minimize False-negative Rate with Ensured True-positive Rate

Koizumi, Y., Murata, S., Harada, N., Saito, S., & Uematsu, H. (2019) · ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2019.8683667 · Cited by 44 · see citing works

Strength in Numbers: Robust Tamper Detection in Crowd Computations

Viswanath, B., Bashir, M. A., Zafar, M. B., Bouget, S., Guha, S., Gummadi, K. P., Kate, A., & Mislove, A. (2015) · Proceedings of the 2015 ACM on Conference on Online Social Networks · doi:10.1145/2817946.2817964 · Cited by 43 · see citing works

Anomaly Analysis of Alzheimer’s Disease in PET Images Using an Unsupervised Adversarial Deep Learning Model

Baydargil, H. B., Park, J.-S., & Kang, D.-Y. (2021) · Applied Sciences · doi:10.3390/app11052187 · Cited by 30 · see citing works

Multi-Dimensional Anomaly Detection and Fault Localization in Microservice Architectures: A Dual-Channel Deep Learning Approach with Causal Inference for Intelligent Sensing

Xing, S., Wang, Y., & Liu, W. (2025) · Sensors · doi:10.3390/s25113396 · Cited by 23 · see citing works

A Framework for Outlier Description Using Constraint Programming

Kuo, C.-T., & Davidson, I. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10174 · Cited by 18 · see citing works

Online Learning of Oil Leak Anomalies in Wind Turbines with Block-Based Binary Reservoir

Cardoni, M., Pau, D. P., Falaschetti, L., Turchetti, C., & Lattuada, M. (2021) · Electronics · doi:10.3390/electronics10222836 · Cited by 17 · see citing works

Mindfulness practice and stress following mindfulness-based stress reduction: Examining within-person and between-person associations with latent curve modeling

McClintock, A. S., Brown, R., Coe, C. L., Zgierska, A., & Barrett, B. (2019) · Mindfulness · doi:10.1007/s12671-019-01159-x · Cited by 7 · see citing works

Anomaly detection and prediction of sensors faults in a refinery using data mining techniques and fuzzy logic

Saybani, M. R. (2011) · Scientific Research and Essays · doi:10.5897/sre11.333 · Cited by 7 · see citing works

Anomaly detection and root-cause identification in microservices: a survey

Barata, L. M., Sequeira, S., Lopes, E., Inácio, P. R. M., & Freire, M. M. (2026) · Cluster Computing · doi:10.1007/s10586-026-06095-9

Outliers Identification Model in Point-of-Sales Data Using Enhanced Normal Distribution Method

Yoseph, F., Heikkilä, M., & Howard, D. (2019) · International Conference on Machine Learning and Data Engineering (iCMLDE) · doi:10.1109/icmlde49015.2019.00024 · Cited by 8 · see citing works

A study of outliers in the exponential smoothing approach to forecasting

Koehler, A. B., Snyder, R. D., Ord, J. K., & Beaumont, A. N. (2011) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2011.05.001 · Cited by 39 · see citing works

The robustness data processing method about eliminating outlier

Lin, H. (2004) · Journal of China Institute of Metrology · Cited by 1 · see citing works

Outlier Robust Prediction

Chambers, R. L., & Clark, R. G. (2012) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780198566625.003.0010

A note on contamination models and outliers

Wellmann, J., & Ursula Gather, C. (1999) · Communication in Statistics- Theory and Methods · doi:10.1080/03610929908832388 · Cited by 6 · see citing works