A comprehensive survey of numeric and symbolic outlier mining techniques

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Mining association rules between sets of items in large databases

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Efficient algorithms for mining outliers from large data sets

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Database mining: a performance perspective

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Support vector clustering

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STING: A Statistical Information Grid Approach to Spatial Data Mining

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Distance-based outliers: algorithms and applications

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An efficient approach to clustering in large multimedia databases with noise

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Outlier detection for high dimensional data

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Fast Outlier Detection in High Dimensional Spaces

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Knowledge discovery and data mining: towards a unifying framework

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WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases

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Outlier Detection Using Replicator Neural Networks

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What makes patterns interesting in knowledge discovery systems

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Mining distance-based outliers in near linear time with randomization and a simple pruning rule

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The Grand Tour: A Tool for Viewing Multidimensional Data

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Enhancing Effectiveness of Outlier Detections for Low Density Patterns

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Question classification using support vector machines

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Distributed data mining in credit card fraud detection

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Separate-and-Conquer Rule Learning

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Survey of fraud detection techniques

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Pruning and summarizing the discovered associations

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Finding Intensional Knowledge of Distance-Based Outliers

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A Linear Method for Deviation Detection in Large Databases

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Outlier mining in large high-dimensional data sets

Angiulli, F., & Pizzuti, C. (2005) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2005.31 · Cited by 363 · see citing works

On subjective measures of interestingness in knowledge discovery

Silberschatz, A., & Tuzhilin, A. (1995) · Cited by 356 · see citing works

An information theoretic approach to rule induction from databases

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Unsupervised Profiling Methods for Fraud Detection

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Mining top-n local outliers in large databases

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A unifying framework for detecting outliers and change points from non-stationary time series data

Yamanishi, K., & Takeuchi, J. (2002) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/775047.775148 · Cited by 283 · see citing works

Outlier detection and motion segmentation

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Computing depth contours of bivariate point clouds

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A unified notion of outliers: properties and computation

Knorr, E. M., & Ng, R. T. (1997) · Cited by 257 · see citing works

A belief-driven method for discovering unexpected patterns

Padmanabhan, B. (1998) · Cited by 242 · see citing works

Web classification using support vector machine

Sun, A., Lim, E., & Ng, W. K. (2002) · ACM International Workshop on Web Information and Data Management (WIDM) · doi:10.1145/584931.584952 · Cited by 222 · see citing works

Fuzzy support vector machines for pattern classification

Inoue, T., & Abe, S. (2002) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2001.939575 · Cited by 221 · see citing works

The interestingness of deviations

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An effective and efficient algorithm for high-dimensional outlier detection

Aggarwal, C. C., & Yu, P. S. (2004) · The VLDB Journal · doi:10.1007/s00778-004-0125-5 · Cited by 212 · see citing works

Novelty detection using extreme value statistics

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OPTICS-OF: Identifying Local Outliers

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Unexpectedness as a measure of interestingness in knowledge discovery

Padmanabhan, B., & Tuzhilin, A. (1999) · Decision Support Systems · doi:10.1016/s0167-9236(99)00053-6 · Cited by 185 · see citing works

Detecting graph-based spatial outliers

Shekhar, S., Lu, C., & Zhang, P. (2001) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/502512.502567 · Cited by 168 · see citing works

Finding interesting patterns using user expectations

Liu, B., Hsu, W., Mun, L.-F., & Lee, H.-Y. (1999) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/69.824588 · Cited by 156 · see citing works

A new measure of overall potential influence in linear regression

Hadi, A. S. (1992) · Computational Statistics & Data Analysis · doi:10.1016/0167-9473(92)90078-t · Cited by 146 · see citing works

Small is beautiful

Padmanabhan, B., & Tuzhilin, A. (2000) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/347090.347103 · Cited by 145 · see citing works

The "DGX" distribution for mining massive, skewed data

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Exception Rule Mining with a Relative Interestingness Measure

Hussain, F., Liu, H., Suzuki, E., & Lü, H. (2000) · Lecture notes in computer science · doi:10.1007/3-540-45571-x_11 · Cited by 114 · see citing works

Outlier Detection Algorithms in Data Mining Systems

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An outlier-based data association method for linking criminal incidents

Lin, S., & Brown, D. E. (2004) · Decision Support Systems · doi:10.1016/j.dss.2004.06.005 · Cited by 85 · see citing works

Discovering unexpected information from your competitors' web sites

Liu, B., Ma, Y., & Yu, P. S. (2001) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/502512.502534 · Cited by 84 · see citing works

Outlier finding

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Scaling Up Inductive Learning with Massive Parallelism

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Outlier Detection Integrating Semantic Knowledge

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Detecting Unusual and Deceptive Communication in Email

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Detecting region outliers in meteorological data

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Parallel Mining of Outliers in Large Database

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ROR: rejection of outliers by rotations

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Fast discovery of unexpected patterns in data, relative to a Bayesian network

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Exceptional knowledge discovery in databases based on information theory

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Extremal dynamics: A unifying physical explanation of fractals, 1/f noise, and activated processes

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Mining web content outliers using structure oriented weighting techniques and N-grams

Agyemang, M., Barker, K. E., & Alhajj, R. S. (2005) · ACM Symposium on Applied Computing · doi:10.1145/1066677.1066788 · Cited by 31 · see citing works

Framework for mining web content outliers

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Detecting Graph-based Spatial Outliers: Algorithms and Applications

Shekhar, S., Lu, C., & Zhang, P. (2001) · University of Minnesota Digital Conservancy (University of Minnesota) · Cited by 28 · see citing works

Beyond Keyword Filtering for Message and Conversation Detection

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Hybrid Approach to Web Content Outlier Mining Without Query Vector

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Anomaly detection: A survey

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A review of novelty detection

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The application of data mining techniques in financial fraud detection: a classification framework and an academic review of literature

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Network Anomaly Detection: Methods, Systems and Tools

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Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection

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

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Video-Based Abnormal Human Behavior Recognition—A Review

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Anomaly detection in dynamic networks: a survey

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Financial fraud detection applying data mining techniques: A comprehensive review from 2009 to 2019

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Review and big data perspectives on robust data mining approaches for industrial process modeling with outliers and missing data

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Local outlier detection reconsidered: a generalized view on locality with applications to spatial, video, and network outlier detection

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Real-Time Deep Anomaly Detection Framework for Multivariate Time-Series Data in Industrial IoT

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Outlier Detection: Applications And Techniques

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Distributed Online One-Class Support Vector Machine for Anomaly Detection Over Networks

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There and back again: Outlier detection between statistical reasoning and data mining algorithms

Zimek, A., & Peter Filzmoser, A. (2018) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1280 · Cited by 188 · see citing works

A Review of Financial Accounting Fraud Detection based on Data Mining Techniques

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Network Anomaly Detection: A Machine Learning Perspective

Bhattacharyya, D. K., & Kalita, J. K. (2013) · Cited by 150 · 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

Algorithmic Reason

Aradau, C., & Blanke, T. (2022) · doi:10.1093/oso/9780192859624.001.0001 · Cited by 113 · see citing works

Self-supervised anomaly detection in computer vision and beyond: A survey and outlook

Hojjati, H., Ho, T. K. K., & Armanfard, N. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106106 · Cited by 126 · see citing works

Governing others: Anomaly and the algorithmic subject of security

Aradau, C., & Blanke, T. (2017) · European Journal of International Security · doi:10.1017/eis.2017.14 · Cited by 99 · see citing works

Anomaly Detection Methods for Categorical Data

Taha, A., & Hadi, A. S. (2019) · ACM Computing Surveys · doi:10.1145/3312739 · Cited by 97 · see citing works

A Survey on Anomaly detection in Evolving Data

Salehi, M., & Rashidi, L. (2018) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3229329.3229332 · Cited by 90 · see citing works

Value-added tax fraud detection with scalable anomaly detection techniques

Vanhoeyveld, J., Martens, D., & Peeters, B. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.105895 · Cited by 91 · see citing works

Anomaly Detection in Smart Environments: A Comprehensive Survey

Fährmann, D., Martín, L., Sánchez, L., & Damer, N. (2024) · IEEE Access · doi:10.1109/access.2024.3395051 · Cited by 80 · see citing works

Mining social networks for anomalies: Methods and challenges

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Enhancing Fault Classification Accuracy of Ball Bearing Using Central Tendency Based Time Domain Features

Tahir, M. M., Khan, A. Q., Iqbal, N., Hussain, A., & Badshah, S. (2016) · IEEE Access · doi:10.1109/access.2016.2608505 · Cited by 75 · see citing works

Robust one-class SVM for fault detection

Xiao, Y., Wang, H., Xu, W., & Zhou, J. (2015) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2015.11.010 · Cited by 72 · see citing works

Anomaly detection on the edge

Schneible, J., & Lu, A. (2017) · IEEE Military Communications Conference (MILCOM) · doi:10.1109/milcom.2017.8170817 · Cited by 69 · 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

Domain Anomaly Detection in Machine Perception: A System Architecture and Taxonomy

Kittler, J., Christmas, W. J., de Campos, T. E., Windridge, D., Yan, F., Illingworth, J., & Osman, M. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.209 · Cited by 51 · see citing works

A survey on Neyman‐Pearson classification and suggestions for future research

Tong, X., Feng, Y., & Zhao, A. (2016) · Wiley Interdisciplinary Reviews Computational Statistics · doi:10.1002/wics.1376 · Cited by 44 · see citing works

Comparison of novelty detection methods for multispectral images in rover-based planetary exploration missions

Kerner, H., Wagstaff, K. L., Bue, B., Wellington, D., Jacob, S., Horton, P., Bell, J. F., III, Kwan, C., & Amor, H. B. (2020) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-020-00697-6 · Cited by 44 · see citing works

Anomaly detection for symbolic sequences and time series data

Kumar, V., Banerjee, A., & Chandola, V. (2009) · Cited by 47 · see citing works

Anomaly detection

SCHNEIDER, P. L., & Xhafa, F. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-823818-9.00013-4 · Cited by 43 · see citing works

Anomaly Detection for Urban Vehicle GNSS Observation with a Hybrid Machine Learning System

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Impact of Outlier Detection on Neural Networks Based Property Value Prediction

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Data-driven approaches for impending fault detection of industrial systems: a review

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Online anomaly detection on the webscope S5 dataset: A comparative study

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A plug-in approach to neyman-pearson classification

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Development of an intelligent information system for financial analysis depend on supervised machine learning algorithms

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Meta-survey on outlier and anomaly detection

Olteanu, M., Fabrice Rossi, A., & Yger, F. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126634 · Cited by 33 · see citing works

Customs fraud detection

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Inter-image outliers and their application to image classification

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Automated novelty detection in the WISE survey with one-class support vector machines

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Machine learning for detection of anomalies in press-hardening: Selection of efficient methods

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Unsupervised Anomaly Detection for High Dimensional Data—an Exploratory Analysis

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