A comprehensive survey of numeric and symbolic outlier mining techniques

Data mining: concepts and techniques

Sharma, R., Shah, K., Shirahatti, Y., Patel, S., Sharma, R., Shah, K., Patel, S., Sharma, R., & Han, J. (2012) · Choice Reviews Online · doi:10.5860/choice.49-3305 · Cited by 28,665

Mining association rules between sets of items in large databases

Agrawal, R., Imieliński, T., & Swami, A. (1993) · Proceedings of the 1993 ACM SIGMOD international conference on Management of data · doi:10.1145/170035.170072 · Cited by 14,907

Exploratory Data Analysis

David, F. N., & Tukey, J. W. (1977) · Biometrics · doi:10.2307/2529486 · Cited by 12,707

Robust Regression and Outlier Detection

Piepel, G. F., Rousseeuw, P. J., & Leroy, A. M. (1989) · Technometrics · doi:10.2307/1268828 · Cited by 6,183

LOF: identifying density-based local outliers

Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000) · ACM SIGMOD Record · doi:10.1145/335191.335388 · Cited by 5,207

Mining association rules between sets of items in large databases

Agrawal, R., Imieliński, T., & Swami, A. (1993) · ACM SIGMOD Record · doi:10.1145/170036.170072 · Cited by 4,551

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

A Survey of Outlier Detection Methodologies

Hodge, V., & Austin, J. (2004) · Artificial Intelligence Review · doi:10.1007/s10462-004-4304-y · Cited by 3,596

Identification of Outliers

Hawkins, D. M. (1980) · doi:10.1007/978-94-015-3994-4 · Cited by 2,982

Outliers in Statistical Data

BARNETT, V. D., Lewis, T., & Abeles, F. F. (1979) · Physics Today · doi:10.1063/1.2995737 · Cited by 2,834

Cost-Sensitive Learning-based Methods for Imbalanced Classification Problems with Applications

Razzaghi, T. (2014) · Journal of International Crisis and Risk Communication Research · Cited by 4

The Study of Fraud Detection in Financial and Credit Institutions with Real Data

Soltaniziba, S., & Balafar, M. A. (2015) · Cited by 4

Automatic Growth Detection of Cell Cultures through Outlier Techniques using 2D Images

Gagniuc, P. A., Ionescu-Tirgoviste, C., & Radulescu, C. H. (2013) · International Journal of Computers Communications & Control · doi:10.15837/ijccc.2013.3.466 · Cited by 3

A New Approach for Outlier Detection in Near Real Time

Eiweck, J., Pattinson, C., Behringer, R., & Seewald, A. K. (2010) · UKSim European Symposium on Computer Modeling and Simulation (EMS) · doi:10.1109/ems.2010.86 · Cited by 3

Anomaly Prediction Based on Machine Learning for Memory-Constrained Devices

Kitagawa, Y., Ishigooka, T., & Azumi, T. (2019) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2018edp7339 · Cited by 3

Between incrementalism and punctuated equilibrium: the case of budget in Poland, 1995–2018

Wordliczek, Ł. (2021) · Central European Journal of Public Policy · doi:10.2478/cejpp-2021-0007 · Cited by 5

A Review of Outliers: Towards a Novel Fuzzy Method for Outlier Detection

Mazidi, A., Roshanfar, F., & Darabad, V. P. (2019) · Cited by 3

Detecting Insider Malicious Activities in Cloud Collaboration Systems

Nair, S. G. S., Cuzzocrea, A., & Balakrishnan, R. (2018) · Fundamenta Informaticae · doi:10.3233/fi-2018-1704 · Cited by 3

An Efficient Anomaly Detection Framework for Electromagnetic Streaming Data

Sun, D., Hu, Y., Shi, Z., Xu, G., & Zhou, W. (2019) · International Conference on Big Data and Computing (ICBDC) · doi:10.1145/3335484.3335521 · Cited by 3

Predictive quality performance control in BPM: proposing a framework for predicting quality anomalies

Saab, N., Helms, R. W., & Zoet, M. (2018) · Procedia Computer Science · doi:10.1016/j.procs.2018.10.094 · Cited by 3

A Novel Technique Using Multiple K-Shingling Based Weighted Dissimilarity Score for Web Content Outlier Mining

Khan, L., Ahmed, M., & Almistarihi, H. (2019) · International journal of intelligent engineering and systems · doi:10.22266/ijies2019.0831.23 · Cited by 2

Method of Fraudster Fingerprint Formation During Mobile Application Installations

Polhul, T., & Yarovyi, A. A. (2019) · IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) · doi:10.1109/idaacs.2019.8924369 · Cited by 2

EXISTING OUTLIER VALUES IN FINANCIAL DATA VIA WAVELET TRANSFORM

Alwadi, H. (2015) · Cited by 2

Online Clustering and Outlier Detection

Wang, B., & Dong, A. (2013) · IGI Global eBooks · doi:10.4018/978-1-4666-2086-5.ch017 · Cited by 2

Solution Pattern for Anomaly Detection in Financial Data Streams

Zakrzewicz, M., Wojciechowski, M., & Gławiński, P. (2019) · Communications in computer and information science · doi:10.1007/978-3-030-30278-8_10 · Cited by 2

Abnormal data detection of power marketing audit based on DSR-Net

Li, Y., Zhang, L., Xu, C., Wang, L., & Qiu, C. (2021) · International Conference on Electronics, Circuits and Information Engineering (ECIE) · doi:10.1109/ecie52353.2021.00027 · Cited by 2

Statistical Analysis and Modeling of Data

Širca, S., & Horvat, M. (2018) · Graduate texts in physics · doi:10.1007/978-3-319-78619-3_5 · Cited by 2

Introduction

Aradau, C., & Blanke, T. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780192859624.003.0001 · Cited by 3

Non-technical Loss Detection with Statistical Profile Images Based on Semi-supervised LearningPreprint

Li, J., & Wang, F. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.03925 · Cited by 2

Exploitation of signal information for mobile speed estimation and anomaly detection

Afgani, M. (2011) · ERA · Cited by 2

Others

Aradau, C., & Blanke, T. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780192859624.003.0004 · Cited by 1

Knowledge

Aradau, C., & Blanke, T. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780192859624.003.0002 · Cited by 1

A Sparse Bayesian Framework for Anomaly Detection in Heterogeneous Networks

Zhang, J., & Kannan, R. (2012) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-642-29222-4_6 · Cited by 1

Sparse Representation Based Anomalies Detection in Electrocardiography Signals

Andrysiak, T. (2017) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-67180-2_42 · Cited by 1

Improving Security of Future Networks Using Enhanced Customer Edge Switching and Risk-Based Analysis

Nowaczewski, S., & Mazurczyk, W. (2021) · Electronics · doi:10.3390/electronics10091107 · Cited by 1

Automated Anomaly Detection in Real-Time Data Streams: An Application at Token Financial Technologies Company

Aslan, D. (2022) · Lecture notes in management and industrial engineering · doi:10.1007/978-3-031-08782-0_19 · Cited by 1

Research on the Derated Power Data Identification Method of a Wind Turbine Based on a Multi-Gaussian–Discrete Joint Probability Model

Ma, Y., Liu, Y., Yang, Z., Yan, J., Tao, T., & Infield, D. G. (2022) · Sensors · doi:10.3390/s22228891 · Cited by 1

Outlier detection

Han, J., Pei, J., & Tong, H. (2023) · Elsevier eBooks · doi:10.1016/b978-0-12-811760-6.00021-7 · Cited by 2

Optymalizacja procesów wnioskowania z wiedzą niepełną

Jach, T. (2013) · Cited by 1

Intelligent Analysis of Database Users Based on A Dynamic Model

Ai, J., Wang, J., Chen, S., Guan, H., Liang, C., & Chen, L. (2017) · International Conference on Machinery, Electronics and Control Simulation (MECS) · doi:10.2991/mecs-17.2017.126 · Cited by 1

Low-Default Portfolio/One-Class Classification: A Literature Review

Kennedy, K., Namee, B. M., & Delany, S. J. (2009) · Arrow@dit (Dublin Institute of Technology) · Cited by 1

Bibliography

(2012) · Elsevier eBooks · doi:10.1016/b978-0-12-381479-1.00015-0 · Cited by 1

An Applied Strategy for Using Empirical and Hybrid Models in Online Monitoring

Rashdan, A. Y. A., USDOE Office of Nuclear Energy (NE), Abdel‐Khalik, H. S., Giraud, K. M., Griffel, L. M., Guillen, D. P., & Varuttamaseni, A. (2020) · doi:10.2172/2376854 · Cited by 1

Statistical Analysis and Modeling of Data

Širca, S., & Horvat, M. (2012) · Graduate texts in physics · doi:10.1007/978-3-642-32478-9_5

Diretrizes metodológicas e validação estatística de dados para a construção de data warehouses

Takecian, P. L. (2014) · doi:10.11606/t.45.2014.tde-10112014-110134

Finding interesting outliers - a Belief Network based approach

Masood, A., & Li, W. (2015) · IEEE SoutheastCon · doi:10.1109/secon.2015.7132918

A methodological approach to mining and simulating data in complex information systems

Sokolova, M. V., & Fernández‐Caballero, A. (2013) · Intelligent Data Analysis · doi:10.3233/ida-130605

Anomaly Detection for Symbolic Representations

Cox, M. T., Paisner, M., Oates, T., & Perlis, D. (2014) · University Libraries (University of Maryland)

Promising techniques for anomaly detection on network traffic

Tian, H., Liu, J., & Ding, M. (2017) · Computer Science and Information Systems · doi:10.2298/csis170201018h

Scalable Algorithms for Mining Dynamic Graphs and Hypergraphs with Applications to Anomaly Detection.

Ranshous, S. (2017) · NCSU Libraries Repository (North Carolina State University Libraries)

Performance Evaluation of Ensemble Method Based Outlier Detection Algorithm

Priya, M., & Karthikeyan, M. (2019) · International Journal of Research in Advent Technology · doi:10.32622/ijrat.732019106

FPGA Architectures for Low Precision Machine Learning

Moss, D. J. M. (2017) · The Sydney eScholarship Repository (The University of Sydney)

A Combined Clustering Method of Suppressed Expectation and Maxi PCA Clustering for Intrusion Detection

Inbaraj, X. A., & Jeng, J.-H. (2020) · International Conference on Inventive Computation Technologies (ICICT) · doi:10.1109/icict48043.2020.9112378

An EffectiveMulti-Layer Model for Controlling the Quality of Data

Leung, C. K.-S., Mateo, M. A. F., & Nadler, A. J. (2007) · International Database Engineering and Applications Symposium (IDEAS) · doi:10.1109/ideas.2007.4318086 · Cited by 6

Signed-With-Weight Technique for Mining Web Content Outliers

Poonkuzhali, S., Sudhakar, P., & Sarukesi, K. (2013)

Mineração de dados como suporte à detecção de lavagem de dinheiro

Paula, E. L. (2016) · doi:10.26512/2016.12.d.22598

Online Clustering and Outlier Detection

Wang, B., & Dong, A. (2012) · IGI Global eBooks · doi:10.4018/978-1-4666-2455-9.ch008

Value

Aradau, C., & Blanke, T. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780192859624.003.0006

Accountability

Aradau, C., & Blanke, T. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780192859624.003.0008

Ethics

Aradau, C., & Blanke, T. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780192859624.003.0007

International

Aradau, C., & Blanke, T. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780192859624.003.0009

Platforms

Aradau, C., & Blanke, T. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780192859624.003.0005

Conclusion

Aradau, C., & Blanke, T. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780192859624.003.0010

Decision

Aradau, C., & Blanke, T. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780192859624.003.0003

Copyright Page

(2022) · Oxford University Press eBooks · doi:10.1093/oso/9780192859624.002.0003

List of Figures

(2022) · Oxford University Press eBooks · doi:10.1093/oso/9780192859624.002.0006

Bridging Disciplinary Divides: Exploring the Synergy of Punctuated Equilibrium Theory and Artificial Neural Networks in Policy Change Analysis

Wordliczek, Ł. (2023) · Barometr Regionalny Analizy i Prognozy · doi:10.56583/br.2191

Identification and Accounting of Aperiodic Anomalies in Time Series with Cyclic Components

Morozova, V., & Menshikh, V. (2023) · Applied Mathematics, Computational Science and Mechanics: Current Problems (AMCSM) · doi:10.1109/amcsm59829.2023.10525830

Bibliography

(2023) · Elsevier eBooks · doi:10.1016/b978-0-12-811760-6.00024-2

Statistical Analysis and Modeling of Data

Širca, S., & Horvat, M. (2025) · Graduate texts in physics · doi:10.1007/978-3-031-68566-8_6

Hybrid Outlier Detection Framework Based on Optimized KMeans and HDBSCAN Using Bat Algorithm and LSTM Autoencoder

Abdelsamie, M., Refaat, H. E., Abdallah, M., & Hassan, O. F. (2025) · Statistics Optimization & Information Computing · doi:10.19139/soic-2310-5070-2581

A domain knowledge informed approach for anomaly detection of electric vehicle interior sounds

Kunte, D., Cornelis, B., Colangeli, C., Janssens, K., Baelen, B. V., & Gryllias, K. (2026) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2026.114634

Méthodes statistiques de détection d’observations atypiques pour des données en grande dimension

Archimbaud, A. (2018) · doi:10.70675/cfb13f08zebc5z4b1ez9329zb16ca6306877

Détection et localisation d'anomalies dans des données hétérogènes en utilisant des modèles graphiques non orientés mixtes

Laby, R. (2017) · doi:10.70675/dcb0816fzdbb3z44d6za621z839e4807db89

Técnica de aprendizado semissupervisionado para detecção de outliers

Zamoner, F. W. (2014) · doi:10.11606/d.55.2014.tde-07042014-100038

Data mining for outliers with robust statistics

Martin, R. D. (2001) · doi:10.1145/502787.502789

Detection of outliers : a data mining approach

Fiott, T. (2015)

A SURVEY OF OUTLIER DETECTION IN DATA MINING

(2015) · International Journal of Advance Engineering and Research Development · doi:10.21090/ijaerd.ncrretcs06 · Cited by 5

Outliers and data mining : finding exceptions in data

Knorr, E. M. (2009) · Open Collections · doi:10.14288/1.0051497 · Cited by 20

Noname manuscript No. (will be inserted by the editor) Mining Outlying Aspects on Numeric Data

Duan, L., Tang, G., Pei, J., Bailey, J., Campbell, A., Tang, C., Duan, L., Tang, C., Tang, G., Pei, J., et al. (2015)