Outlier Detection: Methods, Models, and Classification

Convex Optimization

Boyd, S., & Vandenberghe, L. (2004) · doi:10.1017/cbo9780511804441 · Cited by 56,601 · see citing works

Maximum Likelihood from Incomplete Data Via the <i>EM</i> Algorithm

Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444 · see citing works

Matrix Computations

Golub, G. (2012) · doi:10.56021/9781421407944 · Cited by 34,505 · see citing works

GAN（Generative Adversarial Nets）

淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 19,268 · see citing works

Proceedings of IEEE Conference on Computer Vision and Pattern Recognition

(1993) · doi:10.1109/cvpr.1993 · Cited by 17,989 · see citing works

Algorithm AS 136: A K-Means Clustering Algorithm

Hartigan, J. A., & Wong, M. A. (1979) · Applied Statistics · doi:10.2307/2346830 · Cited by 14,523 · see citing works

Proceedings of the 24th international conference on Machine learning

(2007) · doi:10.1145/1273496 · Cited by 11,869 · 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

Robust principal component analysis?

Candès, E. J., Li, X., Ma, Y., & Wright, J. N. (2011) · Journal of the ACM · doi:10.1145/1970392.1970395 · Cited by 6,914 · see citing works

R-trees

Guttman, A. (1984) · Proceedings - ACM-SIGMOD International Conference on Management of Data · doi:10.1145/602259.602266 · Cited by 6,611 · see citing works

Clustering by fast search and find of density peaks

Rodríguez, Á., & Laio, A. (2014) · Science · doi:10.1126/science.1242072 · Cited by 4,963 · see citing works

Approximate nearest neighbors: towards removing the curse of dimensionality

Indyk, P., & Motwani, R. (1998) · Proceedings of the thirtieth annual ACM symposium on Theory of computing - STOC '98 · doi:10.1145/276698.276876 · Cited by 4,188 · see citing works

LOF: identifying density-based local outliers

Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000) · Proceedings of the 2000 ACM SIGMOD international conference on Management of data · doi:10.1145/342009.335388 · Cited by 4,149 · 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

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 · see citing works

Support Vector Data Description

Tax, D. M. J., & Duin, R. P. W. (2003) · Machine Learning · doi:10.1023/b:mach.0000008084.60811.49 · Cited by 3,553 · see citing works

Recent Trends in Deep Learning Based Natural Language Processing [Review Article]

Young, T., Hazarika, D., Poria, S., & Cambria, E. (2018) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2018.2840738 · Cited by 2,921 · see citing works

Extensions of Lipschitz mappings into a Hilbert space

Johnson, W. B., & Lindenstrauss, J. (1984) · Contemporary mathematics - American Mathematical Society · doi:10.1090/conm/026/737400 · Cited by 2,583 · see citing works

Anomaly-based network intrusion detection: Techniques, systems and challenges

García-Teodoro, P., Díaz-Verdejo, J., Maciá-Fernández, G., & Vázquez, E. (2009) · Computers &amp; Security · doi:10.1016/j.cose.2008.08.003 · Cited by 2,085 · see citing works

Learning from Time-Changing Data with Adaptive Windowing

Bifet, A., & Gavaldà, R. (2007) · SDM · doi:10.1137/1.9781611972771.42 · Cited by 2,052 · see citing works

One-Dimensional Stable Distributions

Zolotarev, V. M. (1986) · Translations of mathematical monographs · doi:10.1090/mmono/065 · Cited by 1,876 · see citing works

Graph based anomaly detection and description: a survey

Akoglu, L., Tong, H., & Koutra, D. (2015) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-014-0365-y · Cited by 1,504 · see citing works

Scalable Nearest Neighbor Algorithms for High Dimensional Data

Muja, M., & Lowe, D. G. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2321376 · Cited by 1,401 · see citing works

Online Learning and Online Convex Optimization

Shalev‐Shwartz, S. (2012) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000018 · Cited by 1,256 · see citing works

Efficient algorithms for mining outliers from large data sets

Ramaswamy, S., Rastogi, R., & Shim, K. (2000) · Proceedings of the 2000 ACM SIGMOD international conference on Management of data · doi:10.1145/342009.335437 · Cited by 1,171 · see citing works

Semi-Supervised Learning (Chapelle, O. et al., Eds.; 2006) [Book reviews]

Chapelle, O., Schölkopf, B., & Zien, E. A. (2009) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2009.2015974 · Cited by 1,102 · see citing works

Discovering cluster-based local outliers

He, Z., Xu, X., & Deng, S. (2003) · Pattern Recognition Letters · doi:10.1016/s0167-8655(03)00003-5 · Cited by 1,060 · see citing works

A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data

Goldstein, M., & Uchida, S. (2016) · PLOS ONE · doi:10.1371/journal.pone.0152173 · Cited by 1,032 · see citing works

Outlier Detection for Temporal Data: A Survey

Gupta, M., Gao, J., Aggarwal, C. C., & Han, J. (2014) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2013.184 · Cited by 997 · see citing works

Anomaly Detection: A Survey

Musa, T. H. A., & Bouras, A. (2021) · Lecture notes in networks and systems · doi:10.1007/978-981-16-2102-4_36 · Cited by 958 · see citing works

Fast Outlier Detection in High Dimensional Spaces

Angiulli, F., & Pizzuti, C. (2002) · Lecture notes in computer science · doi:10.1007/3-540-45681-3_2 · Cited by 880 · see citing works

LOCI: fast outlier detection using the local correlation integral

Papadimitriou, S., Kitagawa, H., Gibbons, P. B., & Faloutsos, C. (2004) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2003.1260802 · Cited by 869 · see citing works

Angle-based outlier detection in high-dimensional data

Kriegel, H., Schubert, M., & Zimek, A. (2008) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1401890.1401946 · Cited by 865 · 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 · cited 3× · see citing works

Proceedings of the 27th International Conference on Very Large Data Bases

Apers, P. M. G., Atzeni, P., Ceri, S., Paraboschi, S., Ramamohanarao, K., & Snodgrass, R. T. (2001) · Cited by 845 · see citing works

Outlier Detection Using Replicator Neural Networks

Hawkins, S., He, H., Williams, G., & Baxter, R. A. (2002) · Lecture notes in computer science · doi:10.1007/3-540-46145-0_17 · Cited by 735 · see citing works

Database-friendly random projections

Achlioptas, D. (2001) · ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems (PODS) · doi:10.1145/375551.375608 · Cited by 712 · see citing works

Feature bagging for outlier detection

Lazarević, A. M., & Kumar, V. (2005) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1081870.1081891 · Cited by 672 · see citing works

Density-based clustering for real-time stream data

Chen, Y., & Tu, L. (2007) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1281192.1281210 · Cited by 577 · see citing works

Progress in Outlier Detection Techniques: A Survey

Wang, H., Bah, M. J., & Hammad, M. A. (2019) · IEEE Access · doi:10.1109/access.2019.2932769 · Cited by 536 · see citing works

LoOP

Kriegel, H., Kröger, P., Schubert, E., & Zimek, A. (2009) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/1645953.1646195 · Cited by 493 · see citing works

Outlier Detection with Autoencoder Ensembles

Chen, J., Sathe, S. K., Aggarwal, C. C., & Turaga, D. S. (2017) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611974973.11 · Cited by 472 · see citing works

Minority report in fraud detection

Phua, C., Alahakoon, D., & Lee, V. (2004) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1007730.1007738 · Cited by 450 · see citing works

Adversarially 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

Ranking Outliers Using Symmetric Neighborhood Relationship

Jin, W., Tung, A. K. H., Han, J., & Wang, W. (2006) · Lecture notes in computer science · doi:10.1007/11731139_68 · Cited by 441 · see citing works

Incremental Local Outlier Detection for Data Streams

Pokrajac, D., Lazarević, A. M., & Latecki, L. J. (2007) · IEEE Symposium on Computational Intelligence and Data Mining (CIDM) · doi:10.1109/cidm.2007.368917 · Cited by 394 · see citing works

HiCS: High Contrast Subspaces for Density-Based Outlier Ranking

Keller, F., Müller, E., & Böhm, K. (2012) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2012.88 · Cited by 383 · see citing works

Toward Supervised Anomaly Detection

Görnitz, N., Kloft, M., Rieck, K., & Brefeld, U. (2013) · Journal of Artificial Intelligence Research · doi:10.1613/jair.3623 · Cited by 378 · see citing works

Loda: Lightweight on-line detector of anomalies

Pevný, T. (2015) · Machine Learning · doi:10.1007/s10994-015-5521-0 · Cited by 363 · see citing works

Two-phase clustering process for outliers detection

Jiang, M., Tseng, S. S., & Su, C.-C. (2001) · Pattern Recognition Letters · doi:10.1016/s0167-8655(00)00131-8 · Cited by 344 · see citing works

Mining top-n local outliers in large databases

Jin, W., Tung, A. K. H., & Han, J. (2001) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/502512.502554 · Cited by 334 · see citing works

Distance-based detection and prediction of outliers

Angiulli, F., Basta, S., & Pizzuti, C. (2006) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2006.29 · Cited by 288 · see citing works

Efficient parallel kNN joins for large data in MapReduce

Zhang, C., Fei-Fei, L., & Jestes, J. (2012) · International Conference on Extending Database Technology (EDBT) · doi:10.1145/2247596.2247602 · Cited by 255 · see citing works

Efficient processing of k nearest neighbor joins using MapReduce

Lü, W., Shen, Y., Chen, S., & Ooi, B. C. (2012) · Proceedings of the VLDB Endowment · doi:10.14778/2336664.2336674 · Cited by 252 · see citing works

Detecting distance-based outliers in streams of data

Angiulli, F., & Fassetti, F. (2007) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/1321440.1321552 · Cited by 249 · see citing works

A Survey of Outlier Detection Methods in Network Anomaly Identification

Gogoi, P., Bhattacharyya, D. K., Borah, B., & Kalita, J. K. (2011) · The Computer Journal · doi:10.1093/comjnl/bxr026 · Cited by 246 · see citing works

Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection

Pang, G., Cao, L., Chen, L., & Liu, H. (2018) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3219819.3220042 · Cited by 234 · see citing works

ALICE: Towards Understanding Adversarial Learning for Joint Distribution MatchingPreprint

Li, C.-Y., Liu, H., Chen, C.-Y., Pu, Y., Chen, L.-Q., Henao, R., & Carin, L. (2017) · Neural Information Processing Systems · doi:10.48550/arxiv.1709.01215 · Cited by 230 · see citing works

An approach to spacecraft anomaly detection problem using kernel feature space

Fujimaki, R., Yairi, T., & Machida, K. (2005) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1081870.1081917 · Cited by 225 · see citing works

Efficient biased sampling for approximate clustering and outlier detection in large data sets

George Kollios, D., Gunopulos, D., Koudas, N., & Berchtold, S. (2003) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2003.1232271 · Cited by 204 · see citing works

Fast mining of distance-based outliers in high-dimensional datasets

Ghoting, A., Parthasarathy, S., & Otey, M. E. (2008) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-008-0093-2 · Cited by 194 · see citing works

Proceedings of 1994 IEEE 10th International Conference on Data Engineering

(1994) · doi:10.1109/icde.1994 · Cited by 172 · see citing works

One class random forests

Désir, C., Bernard, S., Petitjean, C., & Heutte, L. (2013) · Pattern Recognition · doi:10.1016/j.patcog.2013.05.022 · Cited by 153 · see citing works

Advancements of Outlier Detection: A Survey

Zhang, J. (2013) · ICST Transactions on Scalable Information Systems · doi:10.4108/trans.sis.2013.01-03.e2 · Cited by 147 · see citing works

Incorporating Expert Feedback into Active Anomaly Discovery

Das, S., Wong, W., Dietterich, T. G., Fern, A., & Emmott, A. (2016) · Proceedings · doi:10.1109/icdm.2016.0102 · Cited by 146 · see citing works

Detecting outlying subspaces for high-dimensional data: the new task, algorithms, and performance

Zhang, J., & Wang, H. (2006) · Knowledge and Information Systems · doi:10.1007/s10115-006-0020-z · Cited by 142 · see citing works

Approximations to Magic: Finding Unusual Medical Time Series

Lin, J., Keogh, E., Fu, A. W.-C., & Herle, H. M.-L. V. (2005) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms.2005.34 · Cited by 140 · see citing works

A Survey on Urban Traffic Anomalies Detection Algorithms

Djenouri, Y., Belhadi, A., Lin, J. C., Djenouri, D., & Cano, A. (2019) · IEEE Access · doi:10.1109/access.2019.2893124 · Cited by 135 · see citing works

Rapid Distance-Based Outlier Detection via Sampling

Sugiyama, M., & Borgwardt, K. M. (2013) · Cited by 126 · see citing works

Cluster Analysis for Anomaly Detection in Accounting Data: An Audit Approach

Thiprungsri, S., & Vasarhelyi, M. A. (2011) · The International Journal of Digital Accounting Research · doi:10.4192/1577-8517-v11_4 · Cited by 124 · see citing works

Object recognition supported by user interaction for service robots

Makihara, Y., Takizawa, M., Shirai, Y., Miura, J., & Shimada, N. (2003) · doi:10.1109/icpr.2002.1048001 · Cited by 111 · see citing works

Reducing High-Dimensional Data by Principal Component Analysis vs. Random Projection for Nearest Neighbor Classification

Deegalla, S., & Boström, H. (2006) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2006.43 · Cited by 95 · see citing works

An efficient algorithm for distributed density-based outlier detection on big data

Bai, M., Wang, X., Xin, J., & Wang, G. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.05.135 · Cited by 95 · see citing works

DILOF

Na, G. S., Kim, Y., & Yu, H. (2018) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3219819.3220022 · Cited by 91 · 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

AnyOut: Anytime Outlier Detection on Streaming Data

Assent, I., Kranen, P., Baldauf, C., & Seidl, T. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-29038-1_18 · Cited by 87 · see citing works

Clustering ellipses for anomaly detection

Moshtaghi, M., Havens, T. C., Bezdek, J. C., Park, L., Leckie, C. A., Rajasegarar, S., Keller, J. M., & Palaniswami, M. S. (2010) · Pattern Recognition · doi:10.1016/j.patcog.2010.07.024 · Cited by 87 · see citing works

Efficient Anomaly Detection by Isolation Using Nearest Neighbour Ensemble

Bandaragoda, T., Ting, K. M., Albrecht, D. W., Liu, F. T., & Wells, J. R. (2014) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2014.70 · Cited by 81 · see citing works

Semi-Supervised Anomaly Detection with an Application to Water Analytics

Vercruyssen, V., Meert, W., Verbruggen, G., Maes, K., Bäumer, R., & Davis, J. J. (2018) · Proceedings · doi:10.1109/icdm.2018.00068 · Cited by 78 · see citing works

Self-Adaptive Anytime Stream Clustering

Kranen, P., Assent, I., Baldauf, C., & Seidl, T. (2009) · Proceedings · doi:10.1109/icdm.2009.47 · Cited by 73 · see citing works

Outlier detection by sampling with accuracy guarantees

Wu, M., & Jermaine, C. (2006) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1150402.1150501 · Cited by 72 · see citing works

Detection of Significant Sets of Episodes in Event Sequences

Atallah, M. J., Gwadera, R., & Szpankowski, W. (2005) · Proceedings · doi:10.1109/icdm.2004.10090 · Cited by 66 · see citing works

Nonparametric Divergence Estimation with Applications to Machine Learning on DistributionsPreprint

Póczos, B., Xiong, L., & Schneider, J. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1202.3758 · Cited by 66 · see citing works

Distributed Strategies for Mining Outliers in Large Data Sets

Angiulli, F., Basta, S., Lodi, S., & Sartori, C. (2012) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2012.71 · Cited by 64 · see citing works

LeSiNN: Detecting Anomalies by Identifying Least Similar Nearest Neighbours

Pang, G., Ting, K. M., & Albrecht, D. W. (2015) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2015.62 · Cited by 63 · see citing works

LODES: Local Density Meets Spectral Outlier Detection

Sathe, S., & Aggarwal, C. C. (2016) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611974348.20 · Cited by 63 · see citing works

Locality Sensitive Outlier Detection: A ranking driven approach

Wang, Y., Parthasarathy, S., & Tatikonda, S. (2011) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2011.5767852 · Cited by 62 · see citing works

The TV-tree

Ip, L., JagadishH., V., & FaloutsosChristos (1994) · Very Large Data Bases · doi:10.5555/615204.615210 · Cited by 53 · see citing works

Fast and Scalable Outlier Detection with Approximate Nearest Neighbor Ensembles

Schubert, E., Zimek, A., & Kriegel, H. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-18123-3_2 · Cited by 42 · see citing works

Efficient Pruning Schemes for Distance-Based Outlier Detection

Nguyen, H. V., & Gopalkrishnan, V. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04174-7_11 · Cited by 39 · see citing works

Defying the gravity of learning curve: a characteristic of nearest neighbour anomaly detectors

Ting, K. M., Washio, T., Wells, J. R., & Aryal, S. (2016) · Machine Learning · doi:10.1007/s10994-016-5586-4 · Cited by 36 · see citing works

An efficient hyperellipsoidal clustering algorithm for resource-constrained environments

Moshtaghi, M., Rajasegarar, S., Leckie, C. A., & Karunasekera, S. A. (2011) · Pattern Recognition · doi:10.1016/j.patcog.2011.03.007 · Cited by 33 · see citing works

A Relevance Weighted Ensemble Model for Anomaly Detection in Switching Data Streams

Salehi, M., Leckie, C. A., Moshtaghi, M., & Vaithianathan, T. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-06605-9_38 · Cited by 30 · see citing works

Distributed In-Memory Processing of All k Nearest Neighbor Queries

Chatzimilioudis, G., Costa, C., Demetrios Zeinalipour-yazti, A., Lee, W.-C., & Pitoura, E. (2015) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2015.2503768 · Cited by 30 · see citing works

Harnessing the strengths of anytime algorithms for constant data streams

Kranen, P., & Seidl, T. (2009) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-009-0139-0 · Cited by 20 · see citing works

Robust Distributed Anomaly Detection Using Optimal Weighted One-Class Random Forests

Tsou, Y.-L., Chu, H. M., Li, C., & Yang, S. (2018) · Proceedings · doi:10.1109/icdm.2018.00171 · Cited by 19 · see citing works

Toward supervised anomaly detection

GörnitzNico, KloftMarius, RieckKonrad, & BrefeldUlf (2013) · doi:10.5555/2512538.2512545 · Cited by 14 · see citing works

A locality-aware similar information searching scheme

Li, T., Lin, Y., & Shen, H. (2014) · International Journal on Digital Libraries · doi:10.1007/s00799-014-0128-9 · Cited by 6 · see citing works

Deep Learning in Speech Recognition

Iso, K.-I. (2017) · The Brain & Neural Networks · doi:10.3902/jnns.24.27 · Cited by 6 · see citing works

Pivot-Based Distributed K-Nearest Neighbor Mining

Kuhlman, C., Yan, Y., Cao, L., & Rundensteiner, E. A. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-71246-8_51 · Cited by 4 · 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

A Comprehensive Survey on Graph Anomaly Detection With Deep Learning

Ma, X., Wu, J. X., Xue, S., Yang, J. Y., Zhou, C., Sheng, Q. Z., Xiong, H., & Akoglu, L. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3118815 · Cited by 883 · see citing works

Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses

Goldblum, M., Tsipras, D., Xie, C., Chen, X., Schwarzschild, A., Song, D., Madry, A., Li, B., & Goldstein, T. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3162397 · Cited by 362 · see citing works

Future of generative adversarial networks (GAN) for anomaly detection in network security: A review

Lim, W., Yong, K. S. C., Theng, L. B., & Tan, C. L. (2024) · Computers & Security · doi:10.1016/j.cose.2024.103733 · Cited by 177 · see citing works

A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives

Shaukat, K., Alam, T. M., Luo, S., Shabbir, S., Hameed, I. A., Li, J., Abbas, S. K., & Javed, U. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-73100-7_60 · 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

Fish quality evaluation by sensor and machine learning: A mechanistic review

Saeed, R., Feng, H., Wang, X., Zhang, X., & Fu, Z. (2022) · Food Control · doi:10.1016/j.foodcont.2022.108902 · Cited by 125 · see citing works

Distributed denial of service attack prediction: Challenges, open issues and opportunities

de Neira, A. B., Kantarcı, B., & Nogueira, M. (2023) · Computer Networks · doi:10.1016/j.comnet.2022.109553 · Cited by 121 · see citing works

Securing Industrial Control Systems: Components, Cyber Threats, and Machine Learning-Driven Defense Strategies

Nankya, M., Chataut, R., & Akl, R. (2023) · Sensors · doi:10.3390/s23218840 · Cited by 114 · see citing works

Credit risk prediction of SMEs in supply chain finance by fusing demographic and behavioral data

Zhang, W., Yan, S., Li, J., Tian, X., & Yoshida, T. (2022) · Transportation Research Part E Logistics and Transportation Review · doi:10.1016/j.tre.2022.102611 · Cited by 103 · see citing works

Data cleaning and machine learning: a systematic literature review

Côté, P.-O., Nikanjam, A., Ahmed, N., Humeniuk, D. V., & Khomh, F. (2024) · Automated Software Engineering · doi:10.1007/s10515-024-00453-w · Cited by 117 · see citing works

Uncertainty-aware deep learning for reliable health monitoring in safety-critical energy systems

Yao, Y., Han, T., Yu, J., & Xie, M. (2024) · Energy · doi:10.1016/j.energy.2024.130419 · Cited by 96 · see citing works

Deep Graph Anomaly Detection: A Survey and New Perspectives

Qiao, H., Tong, H., An, B., King, I., Aggarwal, C. C., & Pang, G. (2025) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2025.3581578 · Cited by 128 · see citing works

Networking Systems for Video Anomaly Detection: A Tutorial and Survey

Liu, J., Liu, Y., Lin, J., Li, J., Cao, L., Sun, P., Hu, B., Song, L., Boukerche, A., & Leung, V. C. M. (2025) · ACM Computing Surveys · doi:10.1145/3729222 · Cited by 90 · see citing works

Memorizing Structure-Texture Correspondence for Image Anomaly Detection

Zhou, K. Q., Li, J., Xiao, Y., Yang, J., Cheng, J. T., Liu, W., Luo, W., Liu, J., & Gao, S. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3101403 · Cited by 73 · see citing works

A Hybrid Machine-Learning Ensemble for Anomaly Detection in Real-Time Industry 4.0 Systems

Velásquez, D., Perez, E., Oregui, X., Artetxe, A., Manteca, J., Mansilla, J. E., Toro, M., Maiza, M., & Sierra, B. (2022) · IEEE Access · doi:10.1109/access.2022.3188102 · Cited by 76 · see citing works

An Ensemble of Prediction and Learning Mechanism for Improving Accuracy of Anomaly Detection in Network Intrusion Environments

Imran, I., Jamil, F., & Kim, D. (2021) · Sustainability · doi:10.3390/su131810057 · Cited by 71 · 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

Authentication of Herbal Medicines Based on Modern Analytical Technology Combined with Chemometrics Approach: A Review

Liu, C., Zuo, Z., Xu, F., & Wang, Y. (2022) · Critical Reviews in Analytical Chemistry · doi:10.1080/10408347.2021.2023460 · Cited by 63 · see citing works

A procedure for anomaly detection and analysis

Koren, O., Koren, M., & Peretz, O. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105503 · Cited by 57 · see citing works

Financial transaction fraud detector based on imbalance learning and graph neural network

Tong, G., & Shen, J. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110984 · Cited by 54 · see citing works

Measurement Error Prediction of Power Metering Equipment Using Improved Local Outlier Factor and Kernel Support Vector Regression

Ma, J., Teng, Z., Tang, Q., Qiu, W., Yang, Y., & Duan, J. (2021) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2021.3114740 · Cited by 52 · see citing works

Dynamic real–time abnormal energy consumption detection and energy efficiency optimization analysis considering uncertainty

Yin, S., Yang, H., Xu, K., Zhu, C., Zhang, S., & Liu, G. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.118314 · Cited by 47 · see citing works

Local outlier factor for anomaly detection in HPCC systems

Adesh, A., Shobha, G., Shetty, J., & Xu, L. (2024) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2024.104923 · Cited by 41 · see citing works

GBMOD: A granular-ball mean-shift outlier detector

Cheng, S., Su, X., Chen, B., Chen, H., Peng, D., & Yuan, Z. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.111115 · Cited by 38 · see citing works

Market manipulation detection: A systematic literature review

Khodabandehlou, S., & Golpayegani, A. H. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118330 · Cited by 37 · see citing works

Fuzzy Granule Density-Based Outlier Detection With Multi-Scale Granular Balls

Gao, C., Tan, X., Zhou, J., Ding, W., & Pedrycz, W. (2025) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3525003 · Cited by 37 · see citing works

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

Floating offshore wind turbine mooring line sections health status nowcasting: From supervised shallow to weakly supervised deep learning

Coraddu, A., Oneto, L., Walker, J. M., Patryniak, K., Prothero, A., & Collu, M. (2024) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2024.111446 · Cited by 33 · see citing works

Multi-view Outlier Detection via Graphs Denoising

Hu, B., Wang, X., Zhou, P., & Du, L. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.102012 · Cited by 32 · see citing works

A paradigm shift in crisis management: The nexus of AGI‐driven intelligence fusion networks and blockchain trustworthiness

Yue, Y., & Shyu, J. Z. (2024) · Journal of Contingencies and Crisis Management · doi:10.1111/1468-5973.12541 · Cited by 35 · see citing works

An adversarial contrastive autoencoder for robust multivariate time series anomaly detection

Yu, J., Gao, X., Zhai, F., Li, B., Xue, B., Fu, S., Chen, L., & Meng, Z. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.123010 · Cited by 30 · see citing works

A novel outlier detecting algorithm based on the outlier turning points

Huang, J., Cheng, D., & Zhang, S. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120799 · Cited by 29 · see citing works

Autonomy by Design: Preserving Human Autonomy in AI Decision-Support

Buijsman, S., Carter, S. E., & Bermúdez, J. P. (2025) · Philosophy & Technology · doi:10.1007/s13347-025-00932-2 · Cited by 28 · see citing works

Generating collective counterfactual explanations in score-based classification via mathematical optimization

Carrizosa, E., Ramírez-Ayerbe, J., & Morales, D. R. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121954 · Cited by 27 · see citing works

Consistency-guided semi-supervised outlier detection in heterogeneous data using fuzzy rough sets

Chen, B., Yuan, Z., Peng, D., Chen, X., & Chen, H. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.112070 · Cited by 27 · see citing works

A comparative evaluation of clustering-based outlier detection

Vinces, B. V. S., Schubert, E., Zimek, A., & Cordeiro, R. L. F. (2025) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-024-01086-z · Cited by 33 · see citing works

A spatial filtering inspired three-way clustering approach with application to outlier detection

Ali, B., Azam, N., Shah, A., & Yao, J. (2020) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2020.12.003 · Cited by 27 · see citing works

Detecting outliers from pairwise proximities: Proximity isolation forests

Mensi, A., Tax, D. M. J., & Bicego, M. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109334 · Cited by 26 · see citing works

Neighborhood Rough Residual Network–Based Outlier Detection Method in IoT-Enabled Maritime Transportation Systems

Chen, Q., Xie, L., Zeng, L., Jiang, S., Ding, W., Huang, X., & Wang, H. H. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3285615 · Cited by 26 · see citing works

A new particle-swarm-optimization-assisted deep transfer learning framework with applications to outlier detection in additive manufacturing

Fang, J., Wang, Z., Liu, W., Chen, L., & Liu, X. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107700 · Cited by 25 · see citing works

An online fault detection and diagnosis method of sensors in district heating substations based on long short-term memory network and adaptive threshold selection algorithm

Xue, P., Shi, L., Zhou, Z., Liu, J., & Chen, X. (2024) · Energy and Buildings · doi:10.1016/j.enbuild.2024.114009 · Cited by 25 · see citing works

XEMLPD: an explainable ensemble machine learning approach for Parkinson disease diagnosis with optimized features

Khanom, F., Biswas, S. K., Uddin, M. S., & Mostafiz, R. (2024) · International Journal of Speech Technology · doi:10.1007/s10772-024-10152-2 · Cited by 24 · see citing works

Mobility Trace Analysis for Intelligent Vehicular Networks

Celes, C. S. F. D. S., Boukerche, A., & Loureiro, A. A. F. (2021) · ACM Computing Surveys · doi:10.1145/3446679 · Cited by 23 · see citing works

Revisiting streaming anomaly detection: benchmark and evaluation

Cao, Y., Ma, Y., Zhu, Y., & Ting, K. M. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10995-w · Cited by 28 · see citing works

A High-dimensional Outlier Detection Approach Based on Local Coulomb Force

Zhu, P., Zhang, C., Li, X., Zhang, J., & Qin, X. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3172167 · Cited by 23 · see citing works

Integrating Anti-Influenza Virus Activity and Chemical Pattern Recognition to Explore the Quality Evaluation Method of Lonicerae Japonicae Flos

Xie, X., Gu, L., Xu, W., Yu, X., Yin, G., Wang, J., Jin, Y., Wang, L., Wang, B., & Wang, T. (2022) · Molecules · doi:10.3390/molecules27185789 · Cited by 23 · see citing works

Real-time crash prediction on express managed lanes of Interstate highway with anomaly detection learning

Yang, S., Abdel-Aty, M. A., Islam, Z., & Wang, D. (2024) · Accident Analysis & Prevention · doi:10.1016/j.aap.2024.107568 · Cited by 22 · see citing works

Camouflaged Poisoning Attack on Graph Neural Networks

Jiang, C., He, Y., Chapman, R., & Wu, H. (2022) · International Conference on Multimedia Retrieval · doi:10.1145/3512527.3531373 · Cited by 20 · see citing works

A review of deep learning based anomaly detection

Shi, Y., Yang, G., Wang, X. J., Yu, Q., Feng, Y., & Liu, A. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.132383 · Cited by 20 · see citing works

Measurement Error Assessment for Smart Electricity Meters Under Extreme Natural Environmental Stresses

Ma, J., Teng, Z., Tang, Q., Qiu, W., & Yang, Y. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3111872 · Cited by 19 · see citing works

Spatial outlier detection on discrete GNSS velocity fields using robust Mahalanobis-distance-based unsupervised classification

Magyar, B., Kenyeres, A., Tóth, S. J., Hajdu, I., & Horváth, R. (2022) · GPS Solutions · doi:10.1007/s10291-022-01323-2 · Cited by 19 · see citing works

Genetic association analysis in sugarcane (Saccharum spp.) for sucrose accumulation in humid environments in Colombia

Saavedra‐Diaz, C., Trujillo‐Montenegro, J. H., Jaimes, H. A., Londoño, A., Villareal, F. A. S., López, L. O., Valens, C. A. V., López‐Gerena, J., Riascos, J. J., Amaya, Y. M. Q., et al. (2024) · BMC Plant Biology · doi:10.1186/s12870-024-05233-y · Cited by 18 · see citing works

An open dataset of data lineage graphs for data governance research

Chen, Y., Zhao, Y., Li, X., Zhang, J., Long, J., & Zhou, F. (2024) · Visual Informatics · doi:10.1016/j.visinf.2024.01.001 · Cited by 18 · see citing works

A Survey of AI Reliance

Eckhardt, S., Kühl, N., Dolata, M., & Schwabe, G. (2025) · ACM Computing Surveys · doi:10.1145/3776528 · Cited by 29 · see citing works

A Novel Intelligent-Based Intrusion Detection System Approach Using Deep Multilayer Classification

Ugendhar, A., Illuri, B., Vulapula, S. R., Radha, M., Sukanya, K., Alenezi, F. S., Althubiti, S. A., & Polat, K. (2022) · Mathematical Problems in Engineering · doi:10.1155/2022/8030510 · Cited by 22 · see citing works

IDeAuth: A novel behavioral biometric-based implicit deauthentication scheme for smartphones

Gupta, S., Kumar, R., Kacimi, M., & Bruno Crispo, A. (2022) · Pattern Recognition Letters · doi:10.1016/j.patrec.2022.03.011 · Cited by 17 · see citing works

Mutual information maximization for semi-supervised anomaly detection

Liu, S., & Tian, M. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.111196 · Cited by 17 · see citing works

Active Learning for Data Quality Control: A Survey

Li, N., Qi, Y.-Y., Li, C.-R., & Zhao, Z.-M. (2024) · ACM Journal of Data and Information Quality · doi:10.1145/3663369 · Cited by 15 · see citing works

Fast, exact, and parallel-friendly outlier detection algorithms with proximity graph in metric spaces

Amagata, D., Onizuka, M., & Hara, T. (2022) · The VLDB Journal · doi:10.1007/s00778-022-00729-1 · Cited by 16 · see citing works

Insider Threat Detection Model Enhancement Using Hybrid Algorithms between Unsupervised and Supervised Learning

Yi, J., & Tian, Y. (2024) · Electronics · doi:10.3390/electronics13050973 · Cited by 22 · see citing works

Prediction of teaching quality in the context of smart education: application of multimodal data fusion and complex network topology structure

Li, C., Liu, C., Ju, W., Zhong, Y., & Li, Y. (2025) · Discover Artificial Intelligence · doi:10.1007/s44163-025-00240-w · Cited by 16 · see citing works

Multiple Dynamic Outlier-Detection from a Data Stream by Exploiting Duality of Data and Queries

Yoon, S., Shin, Y., Lee, J.-G., & Lee, B. S. (2021) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3448016.3452810 · Cited by 16 · see citing works

Outlier detection using an ensemble of clustering algorithms

Ray, B., Ghosh, S., Ahmed, S., Sarkar, R., & Nasipuri, M. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11671-9 · Cited by 16 · see citing works

DBSCOUT: A Density-based Method for Scalable Outlier Detection in Very Large Datasets

Corain, M., Garza, P., & Asudeh, A. (2021) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde51399.2021.00011 · Cited by 16 · see citing works

ODMGIS: An Outlier Detection Method Based on Multigranularity Information Sets

Zhang, P., He, Z., Wang, D., Jiang, T., Li, B., Liu, J., Huang, W., & Li, T. (2025) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2025.3550749 · Cited by 15 · see citing works

Intelligent multi-channel classification of microseismic events upon TBM excavation

Yin, X. S., Gao, F., Chen, Z., Pan, Y., Liu, Q., & Cheng, S. (2025) · Journal of Rock Mechanics and Geotechnical Engineering · doi:10.1016/j.jrmge.2025.09.002 · Cited by 15 · see citing works

NFAD: fixing anomaly detection using normalizing flows

Ryzhikov, A., Borisyak, M., Ustyuzhanin, A., & Деркач, Д. (2021) · PeerJ Computer Science · doi:10.7717/peerj-cs.757 · Cited by 15 · see citing works

E$^{3}$3Outlier: a Self-Supervised Framework for Unsupervised Deep Outlier Detection

Wang, S., Zeng, Y., Yu, G., Cheng, Z., Liu, X.-W., Zhou, S.-H., Zhu, E., Kloft, M., Yin, J.-P., & Liao, Q. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3188763 · Cited by 22 · see citing works

A survey of streaming data anomaly detection in network security

Zhou, P. (2025) · PeerJ Computer Science · doi:10.7717/peerj-cs.3066 · Cited by 14 · see citing works

A novel anomaly score based on kernel density fluctuation factor for improving the local and clustered anomalies detection of isolation forests

Dong, N., Ren, B., Li, H., Zhong, X., Gong, X., Han, J., Lv, J., & Cheng, J. (2023) · Information Sciences · doi:10.1016/j.ins.2023.118979 · Cited by 14 · see citing works

Deep domain-adversarial anomaly detection with robust one-class transfer learning

Chi, J., & Mao, Z. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112225 · Cited by 14 · see citing works

An ensemble-based outlier detection method for clustered and local outliers with differential potential spread loss

Gao, X., Yu, J., Zha, S., Fu, S., Xue, B., Ye, P., Huang, Z., & Zhang, G. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.110003 · Cited by 13 · see citing works

Identification of Chinese dark jargons in Telegram underground markets using context-oriented and linguistic features

Hou, Y. T., Wang, H., & Wang, H. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.103033 · Cited by 13 · see citing works

A Comparative Study for Outlier Detection Methods in High Dimensional Text Data

Park, C. H. (2022) · Journal of Artificial Intelligence and Soft Computing Research · doi:10.2478/jaiscr-2023-0001 · Cited by 13 · see citing works

Application of Radial Basis Function Neural Network Coupling Particle Swarm Optimization Algorithm to Classification of Saudi Arabia Stock Returns

Rashedi, K. A., Ismail, M. T., Hamadneh, N. N., Wadi, S. A., Jaber, J. J., & Tahir, M. (2021) · Journal of Mathematics · doi:10.1155/2021/5593705 · Cited by 13 · see citing works

Detection of Interictal epileptiform discharges with semi-supervised deep learning

de Sousa, A. M. A., van Putten, M. J. A. M., van den Berg, S. M., & Haeri, M. A. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105610 · Cited by 12 · see citing works

Review on novelty detection in the non-stationary environment

Agrahari, S., Srivastava, S., & Singh, A. K. (2023) · Knowledge and Information Systems · doi:10.1007/s10115-023-02018-x · Cited by 12 · see citing works

Integrating Outlier-Type Prior Knowledge Into Convolutional Neural Networks Based on an Attention Mechanism for Fault Diagnosis

Huang, T., Zhang, Q., Lu, X., Zhao, S., & Shan-lin, Y. (2024) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2024.3461668 · Cited by 12 · see citing works

RPTD: Reliability-enhanced Privacy-preserving Truth Discovery for Mobile Crowdsensing

Liu, Y., Liu, F., Wu, H., Yang, J., Zheng, K., Xu, L., Yan, X., & Hu, J. (2022) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2022.103484 · Cited by 12 · see citing works

Granular-ball computing guided anomaly detection for hybrid attribute data

Su, X., Wang, X., Peng, D., Chen, H., Chen, Y., & Yuan, Z. (2024) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-024-02425-8 · Cited by 11 · see citing works

Boosting the prediction of molten steel temperature in ladle furnace with a dynamic outlier ensemble

Wang, B., Wang, W., Meng, G., Qiao, Z., Guo, Y., Wang, N., Wang, W., & Mao, Z. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105359 · Cited by 11 · see citing works

Anomaly Detection in Electronic Health Records Across Hospital Networks: Integrating Machine Learning With Graph Algorithms

Niu, H., Omitaomu, O. A., Langston, M. A., Grady, S. K., Olama, M. M., Özmen, Ö., Klasky, H. B., Laurio, A., Ward, M. C., & Nebeker, J. R. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3527752 · Cited by 11 · see citing works

Innovative integrated workflow for data-driven production forecasting and well completion optimization: A Montney Formation case study

Rahmanifard, H., & Gates, I. D. (2024) · Geoenergy Science and Engineering · doi:10.1016/j.geoen.2024.212899 · Cited by 9 · see citing works

Fast and robust outlier detection: A granular-ball center isolation and region consistency approach

Wang, R., Wan, J., Li, X., & Tan, S. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.113212 · Cited by 9 · see citing works

Unsupervised Outlier Detection: A Meta-Learning Algorithm Based on Feature Selection

Papastefanopoulos, V., Linardatos, P., & Kotsiantis, S. B. (2021) · Electronics · doi:10.3390/electronics10182236 · Cited by 9 · see citing works

Graph Anomaly Detection at Group Level: A Topology Pattern Enhanced Unsupervised Approach

Ai, X., Zhou, J., Zhu, Y., Li, G., Michalak, T., Luo, X., & Zhou, K. (2024) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde60146.2024.00098 · Cited by 9 · see citing works

RDOD: A Robust Distance-based Technique for Outlier Detection

Gharaei, R. H., & Nezamabadi–pour, H. (2022) · doi:10.1109/icee55646.2022.9827184 · Cited by 9 · see citing works

Cloud Elasticity of Microservices‐Based Applications: A Survey

Fourati, M. H., Marzouk, S., & Jmaïel, M. (2024) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.8329 · Cited by 8 · see citing works

Sustainable extraction of phytochemicals from Mentha arvensis using supramolecular eutectic solvent via microwave Irradiation: Unveiling insights with CatBoost-Driven feature analysis

Naseem, Z., Qadir, M. B., Bentalib, A. S., Khaliq, Z., Zahid, M., Ahmad, F., Nadeem, N., & Javaid, A. (2025) · Ultrasonics Sonochemistry · doi:10.1016/j.ultsonch.2025.107300 · Cited by 8 · see citing works

A novel approach for intelligent fault detection and diagnosis in district heating system: Convergence of machine learning and mathematical statistics

Yang, J., Zhu, J., Peng, M., Cui, X., Li, T., & Liang, X. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.115772 · Cited by 8 · see citing works

Time series to imaging-based deep learning model for detecting abnormal fluctuation in agriculture product price

Jiang, W., Zhang, D., Ling, L., Cai, G., & Zeng, L. (2023) · Soft Computing · doi:10.1007/s00500-023-09121-9 · Cited by 8 · see citing works

A New Reconstruction Method for Measurement Data With Multiple Outliers

Gu, T., Luo, Z., Guo, T., & Luo, T. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3193736 · Cited by 8 · see citing works

Semi-Supervised Active Learning for Anomaly Detection in Aviation

Memarzadeh, M., Matthews, B. L., Templin, T., Rohani, A. S., & Weckler, D. (2023) · Journal of Aerospace Information Systems · doi:10.2514/1.i011083 · Cited by 8 · see citing works

Extraction and Filtering of Electric Network Frequency Using Improved Matrix Pencil and Quadratic Box Plot-Empirical Wavelet Transform

Huang, X., Mingotti, A., Tang, Q., Yang, K., & Teng, Z. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3423483 · Cited by 7 · see citing works

Distributed robust multitask clustering in wireless sensor networks using Multi-Factorial Evolutionary Algorithm

Panwar, A., & Nanda, S. J. (2025) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2025.105038 · Cited by 7 · see citing works

Escape velocity-based adaptive outlier detection algorithm

Yang, L., Yang, L., Tang, D., & Liu, T. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113116 · Cited by 7 · see citing works

A Novel Subspace-Based GMM Clustering Ensemble Algorithm for High-Dimensional Data

He, Y., He, Y., Zhan, Z., Philippe, F.-V., & Huang, J. Z. (2025) · Chinese Journal of Electronics · doi:10.23919/cje.2023.00.153 · Cited by 7 · see citing works

A Survey on Event Prediction Methods from a Systems Perspective: Bringing Together Disparate Research Areas

Benzin, J.-V., & Rinderle‐Ma, S. (2025) · ACM Computing Surveys · doi:10.1145/3743672 · Cited by 13 · see citing works

Fusing fuzzy rough sets and mean shift for anomaly detection

Liao, M., Chen, Z., Gao, C., Zhou, J., & Yue, X. (2025) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2025.109530 · Cited by 7 · see citing works

A survey of outlier detection in high dimensional data streams

Souiden, I., Omri, M. N., & Brahmi, Z. (2022) · Computer Science Review · doi:10.1016/j.cosrev.2022.100463 · Cited by 99 · see citing works

A Comparison of Outlier Detection Techniques for High-Dimensional Data

Xu, X., Liu, H., Li, L., & Yao, M. (2018) · International Journal of Computational Intelligence Systems · doi:10.2991/ijcis.11.1.50 · Cited by 52 · see citing works

In pursuit of outliers in multi-dimensional data streams

Sadik, M. S., Gruenwald, L., & Leal, E. (2016) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2016.7840642 · Cited by 9 · see citing works

Continuous Outlier Detection over Distributed Data Streams

Wang, S. (2008) · Microelectronics & Computer

New methods in outlier detection

Tang, G. (2015) · Summit (Simon Fraser University) · Cited by 2 · see citing works