A survey on unsupervised outlier detection in high‐dimensional numerical data
Abstract
Abstract High‐dimensional data in Euclidean space pose special challenges to data mining algorithms. These challenges are often indiscriminately subsumed under the term ‘curse of dimensionality’, more concrete aspects being the so‐called ‘distance concentration effect’, the presence of irrelevant attributes concealing relevant information, or simply efficiency issues. In about just the last few years, the task of unsupervised outlier detection has found new specialized solutions for tackling high‐dimensional data in Euclidean space. These approaches fall under mainly two categories, namely considering or not considering subspaces (subsets of attributes) for the definition of outliers. The former are specifically addressing the presence of irrelevant attributes, the latter do consider the presence of irrelevant attributes implicitly at best but are more concerned with general issues of efficiency and effectiveness. Nevertheless, both types of specialized outlier detection algorithms tackle challenges specific to high‐dimensional data. In this survey article, we discuss some important aspects of the ‘curse of dimensionality’ in detail and survey specialized algorithms for outlier detection from both categories. © 2012 Wiley Periodicals, Inc. Statistical Analysis and Data Mining, 2012
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Self-citationIs the Distance Compression Effect Overstated? Some Theory and Experimentation
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Self-citationDistance‐preserving dimensionality reduction
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Self-citationSubspace Clustering Techniques
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“Independent of the algorithm, the parameter ε of DBSCAN becomes hard to choose in highdimensional data due to the loss of contrast in distances [9, 25, 48].”
BackgroundResultSelf-citationDeep 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
“As a popular area, a number of studies [2, 4, 16, 28, 53, 62, 178] have been dedicated to the categorization and review of anomaly detection techniques.”
BackgroundInfluentialRobust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network
Su, Y., Zhao, Y., Niu, C., Liu, R., Sun, W., & Pei, D. (2019) · Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3292500.3330672 · Cited by 1,804 · see citing works
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BackgroundGong, D., Liu, L., Le, V., Saha, B., Mansour, M. R., Venkatesh, S., & Van Den Hengel, A. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00179 · Cited by 1,930 · see citing works
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BackgroundMethodInfluentialGraph based anomaly detection and description: a survey
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“detection due to the following factors [7]: (i) Exponential search”
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A Unifying Review of Deep and Shallow Anomaly Detection
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BackgroundInfluentialAn Introduction to Machine Learning
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“In particular, analysis of high-dimensional datasets can be associated with a phenomenon known as the “curse of dimensionality,” 21 which refers to data sparsity and counterintuitive geometrical properties in high-dimensional”
Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection
Campello, R. J. G. B., Moulavi, D., Zimek, A., & Sander, J. (2015) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/2733381 · Cited by 915 · see citing works
“Some authors criticize the use of clustering algorithms as an approach to detect outliers [Knorr and Ng 1998; Ramaswamy et al. 2000; Knorr et al. 2000; Tang et al. 2002; Kriegel et al. 2010; Zimek et al. 2012].”
BackgroundMethodSelf-citationOn the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study
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“Since it is often presumed that the dimensionality of a dataset can be a contributing factor in how well a method performs on the dataset (Houle et al. 2010; Zimek et al. 2012), we first address the question whether this is the case for the given datasets and methods.”
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Self-citationFinancial Fraud: A Review of Anomaly Detection Techniques and Recent Advances
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Progress in Outlier Detection Techniques: A Survey
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“high dimensional numeric data computation [27], [46], [47], there are some critical factors like the curse of dimensionality that needs to be considered.”
BackgroundEfficient GAN-Based Anomaly DetectionPreprint
Zenati, H., Foo, C.-S., Lecouat, B., Manek, G., & Chandrasekhar, V. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.06222 · Cited by 517 · see citing works
“Popular techniques utilize clustering approaches or nearest neighbor methods (Xiong et al., 2011; Zimek et al., 2012) and one class classification approaches that learn a discriminative boundary around normal data, such as one-class SVMs (Yunqiang Chen & Huang, 2001).”
MethodClassification-Reconstruction Learning for Open-Set Recognition
Yoshihashi, R., Shao, W. Q., Kawakami, R., You, S., Iida, M., & Naemura, T. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00414 · Cited by 499 · see citing works
“This bottlenecking is crucial, because outliers are harder to detect in higher dimensional feature spaces due to concentration on the sphere [52].”
BackgroundThe Heterogeneity Problem: Approaches to Identify Psychiatric Subtypes
Feczko, E., Miranda-Dominguez, O., Marr, M., Graham, A. M., Nigg, J. T., & Fair, D. A. (2019) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2019.03.009 · Cited by 443 · see citing works
“On the other hand, if one assumes that the population is comprised of multiple subtypes with different underlying distributions[69,72], then instead of detecting outliers one aims to distinguish boundaries between subtypes.”
BackgroundThe curse(s) of dimensionality
Altman, N., & Krzywinski, M. I. (2018) · Nature Methods · doi:10.1038/s41592-018-0019-x · Cited by 478 · see citing works
A comparative evaluation of outlier detection algorithms: Experiments and analyses
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“Our paper extends previous works [7, 33] by using 12 publicly available labeled datasets, most of which are recommended for outlier detection in [7], in addition to 3 novel industrial datasets from the production environments of a major company in the travel industry.”
MethodDeep Anomaly Detection Using Geometric TransformationsPreprint
Golan, I., & El‐Yaniv, R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.10917 · Cited by 479 · see citing works
“The literature related to anomaly detection is extensive and beyond the scope of this paper (see, e.g., [5, 37] for wider scope surveys).”
BackgroundAdversarially Learned Anomaly Detection
Zenati, H., Romain, M., Foo, C.-S., Lecouat, B., & Chandrasekhar, V. (2018) · Proceedings · doi:10.1109/icdm.2018.00088 · Cited by 450 · see citing works
A comprehensive survey of anomaly detection techniques for high dimensional big data
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Loda: Lightweight on-line detector of anomalies
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“We refer to Zimek et al. (2012) for a comprehensive review of the prior art.”
BackgroundLearning Regularity in Skeleton Trajectories for Anomaly Detection in Videos
de Morais, R. F. A. B., Le, V., Tran, T., Saha, B., Mansour, M. R., & Venkatesh, S. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.01227 · Cited by 351 · see citing works
“Unfortunately, pixel-based features are high-dimensional unstructured signals sensitive to noise, that mask important information about the scene [30].”
BackgroundDeep Structured Energy Based Models for Anomaly Detection
Zhai, S., Cheng, Y., Lu, W., & Zhang, Z. (2016) · arXiv · doi:10.48550/arxiv.1605.07717 · Cited by 334 · see citing works
“A variety of methods can be found in the survey (Zimek et al., 2012).”
BackgroundMethodOutlier 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
“[34] focused on high-dimensional numerical data.”
BackgroundShort-Term Solar Power Forecasting Based on Weighted Gaussian Process Regression
Sheng, H., Xiao, J., Cheng, Y., Ni, Q., & Wang, S. (2017) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2017.2714127 · Cited by 325 · see citing works
“Outlier detection for high-dimensional data has been a challenging topic [28].”
BackgroundSchubert, E., Zimek, A., & Kriegel, H. (2012) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-012-0300-z · Cited by 302 · see citing works
“…published over the last decade (e.g. Hodge and Austin 2004; Agyemang et al. 2006; Hadi et al. 2009; Chandola et al. 2009, 2012; Su and Tsai 2011; Zimek et al. 2012), covering a broad selection of algorithmic approaches to outlier detection and quite different application scenarios, there has…”
BackgroundSelf-citationEmpowering Metasurfaces with Inverse Design: Principles and Applications
Li, Z., Pestourie, R., Lin, Z., Johnson, S. G., & Capasso, F. (2022) · ACS Photonics · doi:10.1021/acsphotonics.1c01850 · Cited by 283 · see citing works
Ensembles for unsupervised outlier detection
Zimek, A., Campello, R. J. G. B., & Sander, J. (2014) · ACM SIGKDD Explorations Newsletter · doi:10.1145/2594473.2594476 · Cited by 287 · see citing works
Self-citationClustering Driven Deep Autoencoder for Video Anomaly Detection
Chang, Y., Tu, Z., Xie, W. C., & Yuan, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58555-6_20 · Cited by 280 · see citing works
Reverse Nearest Neighbors in Unsupervised Distance-Based Outlier Detection
Radovanović, M., Νανόπουλος, Α., & Ivanović, M. D. (2014) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2014.2365790 · Cited by 249 · see citing works
“Index Terms—Outlier detection, reverse nearest neighbors, high-dimensional data, distance concentration Ç”
BackgroundInfluentialVideo anomaly detection with spatio-temporal dissociation
Chang, Y., Tu, Z., Xie, W. C., Luo, B., Zhang, S., Sui, H., & Yuan, J. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108213 · Cited by 239 · see citing works
Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection
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“Such ultrahigh-dimensional data presents significant challenges to existing outlier detection methods due to the curse of dimensionality [38].”
BackgroundAnalysis of network traffic features for anomaly detection
Iglesias, F., & Zseby, T. (2014) · Machine Learning · doi:10.1007/s10994-014-5473-9 · Cited by 208 · see citing works
“This problemhas a strong effect on some classification techniques, yet in clustering and anomaly detection in general (Zimek et al. 2012).”
BackgroundDivide-and-Assemble: Learning Block-wise Memory for Unsupervised Anomaly Detection
Hou, J., Zhang, Y., Zhong, Q., Xie, D., Pu, S., & Zhou, H. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00867 · Cited by 202 · see citing works
Subsampling for efficient and effective unsupervised outlier detection ensembles
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Self-citationThere 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
“• Fundamental challenges of high dimensional data for outlier detection have been discussed by Zimek et al.[228], and variousmethods dedicated to outlier detection in high dimensional data have been proposed.”
InfluentialSelf-citationYairi, T., Takeishi, N., Oda, T., Nakajima, Y., Nishimura, N., & Takata, N. (2017) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2017.2671247 · Cited by 185 · see citing works
“In the context of anomaly detection, it is known that the difference in the distance between anomalous and normal samples and that between normal samples becomes indistinct as the dimensionality becomes higher [21].”
BackgroundNicholls, J., Kuppa, A., & Le‐Khac, N. (2021) · IEEE Access · doi:10.1109/access.2021.3134076 · Cited by 181 · see citing works
“Applicable Algorithms [39] Survey on unsupervised AD techniques.”
MethodInfluentialLearning Neural Representations for Network Anomaly Detection
Cao, V. L., Nicolau, M., & McDermott, J. (2018) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2018.2838668 · Cited by 181 · see citing works
“The curse of dimensionality is a problem for anomaly detection algorithms [11].”
BackgroundRecent Advances in Anomaly Detection Methods Applied to Aviation
Basora, L., Olivé, X., & Dubot, T. (2019) · Aerospace · doi:10.3390/aerospace6110117 · Cited by 179 · see citing works
Isolation‐based anomaly detection using nearest‐neighbor ensembles
Bandaragoda, T., Ting, K. M., Albrecht, D. W., Liu, F. T., Zhu, Y., & Wells, J. R. (2018) · Computational Intelligence · doi:10.1111/coin.12156 · Cited by 179 · see citing works
“From each cluster, 2 % of the randomly selected instances are converted into anomalies by adding or subtracting an offset, similar to the method used in the work of Zimek et al. 26 The percentage of relevant dimensions ( r ) are increased in the range of 1 % , 2 % , … , 50 % .”
MethodSupervised machine learning tools: a tutorial for clinicians
Vercio, L. L., Amador, K., Bannister, J. J., Crites, S., Gutierrez, A., MacDonald, M. E., Moore, J. A., Mouchès, P., Rajashekar, D., Schimert, S., et al. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/abbff2 · Cited by 167 · see citing works
“…multivariate data: while boxplots or z-scores analysis are appropriate for univariate data, higher dimen-sion data requires more complex algorithms [30]; (ii) Parametric vs. non-parametric data: if the underlying distribution of the data is known, outliers can be detected by fitting the…”
MethodChemoinformatic Classification Methods and their Applicability Domain
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On the Effectiveness of Image Rotation for Open Set Domain Adaptation
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Spatiotemporal consistency-enhanced network for video anomaly detection
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“1D) ( 16 ).”
A Comprehensive Survey of Anomaly Detection Algorithms
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Machine learning-guided discovery and design of non-hemolytic peptides
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Generalized Outlier Detection with Flexible Kernel Density Estimates
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“A recently very active domain is subspace outlier detection, tackling the challenges of outlier detection in complex and high-dimensional data [34, 10].”
BackgroundSelf-citationOn Accurate and Reliable Anomaly Detection for Gas Turbine Combustors: A Deep Learning Approach
Yan, W., & Yu, L. (2015) · Annual Conference of the PHM Society · doi:10.36001/phmconf.2015.v7i1.2655 · Cited by 147 · see citing works
Systematic construction of anomaly detection benchmarks from real data
Emmott, A., Das, S., Dietterich, T. G., Fern, A., & Wong, W. (2013) · doi:10.1145/2500853.2500858 · Cited by 145 · see citing works
“Although a wide variety of anomaly detection algorithms have been developed and applied to these tasks [20, 11, 5, 31], a shortcoming of most published work is that there is no standard methodology for comparing anomaly detection methods.”
MethodCampello, R. J. G. B., Kröger, P., Sander, J., & Zimek, A. (2019) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1343 · Cited by 142 · see citing works
“However, with the same amount of data a density estimate that is not corrected for the dimensionality—like the distance to the k th nearest neighbor or the number of points within a ball of fixed radius r or within a bin (hypercube) of fixed side length, which is what most traditional methods are using—will necessaril…”
BackgroundSelf-citationReward Motivation Enhances Task Coding in Frontoparietal Cortex
Etzel, J. A., Cole, M. W., Zacks, J. M., Kay, K. N., & Braver, T. S. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhu327 · Cited by 132 · see citing works
“” Approaches for quantifying and compensating for distance concentration have been rapidly gaining attention within the machine learning community (Radovanovi c et al. 2010; Zimek et al. 2012;Winkler et al. 2013); however, we believe that this is the first use of distance concentrationmetrics to”
BackgroundMachine Learning at Central BanksPreprint
Chakraborty, C., & Joseph, A. C. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.3031796 · Cited by 138 · see citing works
A Hybrid Semi-Supervised Anomaly Detection Model for High-Dimensional Data
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“We refer the readers to good survey papers [3, 19–21] for more details.”
MethodChronoR: Rotation Based Temporal Knowledge Graph Embedding
Sadeghian, A., Armandpour, M., Colas, A., & Wang, D. Z. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i7.16802 · Cited by 9 · see citing works
AutoML: state of the art with a focus on anomaly detection, challenges, and research directions
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“In [89], Zimek et al. discussed the underlying challenge and review some anomaly detection methods, mainly the ones based on approximate neighborhoods, that handle such data.”
BackgroundMethodA 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
SafeDrive: Online Driving Anomaly Detection From Large-Scale Vehicle Data
Zhang, M., Chen, C., Wo, T., Xie, T., Bhuiyan, M. Z. A., & Lin, X. (2017) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2017.2674661 · Cited by 127 · see citing works
“In summary, although there exist various previous techniques [24]–[26] of unsupervised or semisupervised anomaly detection, in driving-analysis scenarios, previous work is mainly based on the definition of driving behaviors, such as rules or patterns.”
MethodVarietal Classification of Rice Seeds Using RGB and Hyperspectral Images
Fabiyi, S. D., Vu, H., Tachtatzis, C., Murray, P., Harle, D., Dao, T., Andonović, I., Ren, J., & Marshall, S. (2020) · IEEE Access · doi:10.1109/access.2020.2969847 · Cited by 127 · see citing works
Rapid Distance-Based Outlier Detection via Sampling
Sugiyama, M., & Borgwardt, K. M. (2013) · Cited by 126 · see citing works
Outlier Detection in Arbitrarily Oriented Subspaces
Kriegel, H., Kröger, P., Schubert, E., & Zimek, A. (2012) · Proceedings · doi:10.1109/icdm.2012.21 · Cited by 119 · see citing works
“A recent survey [3] is discussing subspace outlier detection in a broader perspective.”
BackgroundSelf-citationUnsupervised Sequential Outlier Detection With Deep Architectures
Lu, W., Cheng, Y., Xiao, C., Chang, S., Huang, S., Liang, B., & Huang, T. S. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2713048 · Cited by 121 · see citing works
“The problem of outlier detection has been approached from a number of different perspectives, including (1) the sample reconstruction based methods such as PCA and Kernel PCA as well as Robust PCA and Robust Kernel PCA [23]; (2) the probability density based methods which includes parametric estimators [24] and non-pa…”
MethodDekhtiar, J., Durupt, A., Bricogne, M., Eynard, B., Rowson, H., & Kiritsis, D. (2018) · Computers in Industry · doi:10.1016/j.compind.2018.04.005 · Cited by 119 · see citing works
Unsupervised Novelty Detection Using Deep Autoencoders with Density Based Clustering
Amarbayasgalan, T., Jargalsaikhan, B., & Ryu, K. H. (2018) · Applied Sciences · doi:10.3390/app8091468 · Cited by 116 · see citing works
“But the cluster-based method performance is very dependent on which algorithm is used as well as high-dimensional data in clustering with distance is special challenges to data mining algorithms [13].”
Sliding Window-Based Fault Detection From High-Dimensional Data Streams
Zhang, L., Lin, J., & Karim, R. (2016) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2016.2585566 · Cited by 114 · see citing works
“The “curse of dimensionality” may cause the deterioration of many fault detection techniques because the degree of data abnormality in fault-relevant dimensions can be obscured or even masked by irrelevant attributes [9], [13], [14].”
BackgroundComplementary Set Variational Autoencoder for Supervised Anomaly Detection
Kawachi, Y., Koizumi, Y., & Harada, N. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8462181 · Cited by 109 · see citing works
“To avoid the curse of dimensionality [27], an anomaly score calculated in lowdimensional latent space such as KL divergence may be preferable.”
MethodRecent Progress of Anomaly Detection
Xu, X., Liu, H., & Yao, M. (2019) · Complexity · doi:10.1155/2019/2686378 · Cited by 112 · see citing works
“Tables 4 and 5 show the AUC scores of the anomaly detection algorithms with k =10 and k =50, respectively.”
BackgroundInfluentialExplaining Outliers by Subspace Separability
Micenkova, B., Ng, R. T., Dang, X.-H., & Assent, I. (2013) · 2013 IEEE 13th International Conference on Data Mining · doi:10.1109/icdm.2013.132 · Cited by 104 · see citing works
“However, it has been recently shown [16] that for other distributions the variance of scores decreases with dimensionality and therefore normalization is still needed in order to compare scores from different subspaces.”
BackgroundUnsupervised One-Class Learning for Automatic Outlier Removal
Liu, W., Hua, G., & Smith, J. R. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.483 · Cited by 104 · see citing works
“Complying with this principle , tremendous probability density estimation schemes have been explored, including parametric estimators [32] and nonparametric estimators such as the kernel density ) and the more recent robust kernel density estimator (RKDE) [11].”
BackgroundSerendip: Topic model-driven visual exploration of text corpora
Alexander, E., Kohlmann, J., Valenza, R., Witmore, M., & Gleicher, M. L. (2014) · IEEE Conference on Visual Analytics Science and Technology (VAST) · doi:10.1109/vast.2014.7042493 · Cited by 103 · see citing works
“In general, meaningful distance metrics in high-dimensional spaces are challenging (see [38] for a survey of the issues).”
BackgroundIoT-based predictive maintenance for fleet management
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Efficient Outlier Detection for High-Dimensional Data
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A Novel Outlier Detection Method for Multivariate Data
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Anomaly Detection Methods for Categorical Data
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“Anom-aly detection for high-dimensional data are given in References [4, 188].”
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A survey of outlier detection in high dimensional data streams
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Dimensionality, Discriminability, Density and Distance Distributions
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“Data mining methods that rely on cumulative density as an estimate of local distribution, such as LOF outlier detection [22], [23] or density-based clustering algorithms such as DBSCAN [26], could be reconsidered in light of this relationship.”
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Semi-supervised anomaly detection with dual prototypes autoencoder for industrial surface inspection
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Efficient and flexible algorithms for monitoring distance-based outliers over data streams
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A novelty detection patent mining approach for analyzing technological opportunities
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Memory Residual Regression Autoencoder for Bearing Fault Detection
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“However, the anomaly detection performance of classification-based method can be affected when the data points lay in a high-dimensional space, as modeling the high-dimensional data is notoriously challenging [18], [19].”
BackgroundEfficient Anomaly Detection by Isolation Using Nearest Neighbour Ensemble
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BackgroundMethodFast clustering using adaptive density peak detection
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“Zimek et al.(28) have recently shown that, when variables are correlated, the distance measures can become efficient and provide higher contrast.”
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Discriminative features for identifying and interpreting outliers
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“Here, we focus on the unsupervised setting on numerical high dimensional datasets [37].”
BackgroundResultInfluentialSelf-citationAnomaly Detection with Partially Observed Anomalies
Zhang, Y., Li, L., Zhou, J., Li, X., & Zhou, Z. (2018) · International World Wide Web Conference (WWW) · doi:10.1145/3184558.3186580 · Cited by 78 · see citing works
“Among the developed methods, unsupervised learning based methods [4, 37] build themodel with unlabeled data.”
MethodAttention-based anomaly detection in multi-view surveillance videos
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Outlier Ranking via Subspace Analysis in Multiple Views of the Data
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“Let us start with a baseline scoring function: Definition 3: Individual Weighting (IW) Extending Def.”
BackgroundMining social networks for anomalies: Methods and challenges
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Graph-based Anomaly Detection and Description: A SurveyPreprint
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Tripathi, S., Muhr, D., Manuel, B., Jodlbauer, H., Dehmer, M., & Emmert‐Streib, F. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.576892 · Cited by 85 · see citing works
“High-dimensional data are a challenge for model robustness, outlier detection [128], time, and algorithmic complexity in ML.”
BackgroundMining outlying aspects on numeric data
Duan, L., Tang, G., Pei, J., Bailey, J., Campbell, A., & Tang, C. (2015) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-014-0398-2 · Cited by 70 · see citing works
“Several recent surveys on the topic (Aggarwal 2013; Chandola et al. 2009; Zimek et al. 2012), as well as dedicated chapters in classical data mining textbooks (Han et al. 2011) provide thorough treatments.”
Background
- Authors
- Zimek, A., Schubert, E., & Kriegel, H.
- Affiliations
- University of Alberta (CA)
- Ludwig-Maximilians-Universität München (DE)
- Corresponding author
- Zimek, Arthur
- Venue
- Statistical Analysis and Data Mining The ASA Data Science Journal
- Publisher
- Wiley
- Published
- 2012-08-27
- Volume
- 5
- Issue
- 5
- Pages
- 363-387
- ISSN
- 1932-1864 (ISSN-L)
- 1932-1872
- OpenAlex
- W2015887370
- Cited by
- 864
- References
- 166
- Citations by year
- 2026: 42 · 2025: 62 · 2024: 68 · 2023: 71 · 2022: 72 · 2021: 100 · 2020: 79 · 2019: 94 · 2018: 79 · 2017: 52 · 2016: 68 · 2015: 33 · 2014: 26 · 2013: 13 · 2012: 4
- FWCI
- 23.75
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Statistical Analysis and Data Mining The ASA Data Science Journal (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Clustering Algorithms Research
- Keywords
- unsupervised anomaly detection, high-dimensional data, curse of dimensionality, irrelevant attributes, Euclidean space, outlier detection algorithms
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