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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study
Guilherme O. Campos, Arthur Zimek, Jörg Sander, et al.
Data Mining and Knowledge Discovery (2016) Vol. 30, Iss. 4, pp. 891-927
Closed Access | Times Cited: 648

Showing 1-25 of 648 citing articles:

Deep Learning for Anomaly Detection
Guansong Pang, Chunhua Shen, Longbing Cao, et al.
ACM Computing Surveys (2021) Vol. 54, Iss. 2, pp. 1-38
Open Access | Times Cited: 1535
A Unifying Review of Deep and Shallow Anomaly Detection
Lukas Ruff, Jacob Kauffmann, Robert A. Vandermeulen, et al.
Proceedings of the IEEE (2021) Vol. 109, Iss. 5, pp. 756-795
Open Access | Times Cited: 690
Progress in Outlier Detection Techniques: A Survey
Hongzhi Wang, Mohamed Jaward Bah, Mohamed Hammad
IEEE Access (2019) Vol. 7, pp. 107964-108000
Open Access | Times Cited: 426
Generative Adversarial Active Learning for Unsupervised Outlier Detection
Yezheng Liu, Zhe Li, Chong Zhou, et al.
IEEE Transactions on Knowledge and Data Engineering (2019), pp. 1-1
Open Access | Times Cited: 296
COPOD: Copula-Based Outlier Detection
Zheng Li, Yue Zhao, Nicola Botta, et al.
2021 IEEE International Conference on Data Mining (ICDM) (2020), pp. 1118-1123
Open Access | Times Cited: 261
A local density-based approach for outlier detection
Bo Tang, Haibo He
Neurocomputing (2017) Vol. 241, pp. 171-180
Open Access | Times Cited: 255
Smart anomaly detection in sensor systems: A multi-perspective review
Laura Erhan, Maryleen Ndubuaku, Mario Di Mauro, et al.
Information Fusion (2020) Vol. 67, pp. 64-79
Open Access | Times Cited: 217
The (black) art of runtime evaluation: Are we comparing algorithms or implementations?
Hans‐Peter Kriegel, Erich Schubert, Arthur Zimek
Knowledge and Information Systems (2016) Vol. 52, Iss. 2, pp. 341-378
Closed Access | Times Cited: 212
Multivariate Time Series Anomaly Detection and Interpretation using Hierarchical Inter-Metric and Temporal Embedding
Zhihan Li, Youjian Zhao, Jiaqi Han, et al.
(2021), pp. 3220-3230
Closed Access | Times Cited: 202
ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions
Zheng Li, Yue Zhao, Xiyang Hu, et al.
IEEE Transactions on Knowledge and Data Engineering (2022) Vol. 35, Iss. 12, pp. 12181-12193
Open Access | Times Cited: 199
Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection
Guansong Pang, Longbing Cao, Ling Chen, et al.
(2018), pp. 2041-2050
Open Access | Times Cited: 193
Testing machine learning based systems: a systematic mapping
Vincenzo Riccio, Gunel Jahangirova, Andrea Stocco, et al.
Empirical Software Engineering (2020) Vol. 25, Iss. 6, pp. 5193-5254
Open Access | Times Cited: 179
There and back again: Outlier detection between statistical reasoning and data mining algorithms
Arthur Zimek, Peter Filzmoser
Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery (2018) Vol. 8, Iss. 6
Open Access | Times Cited: 169
Deep Isolation Forest for Anomaly Detection
Hongzuo Xu, Guansong Pang, Yijie Wang, et al.
IEEE Transactions on Knowledge and Data Engineering (2023) Vol. 35, Iss. 12, pp. 12591-12604
Open Access | Times Cited: 140
Mean-shift outlier detection and filtering
Jiawei Yang, Susanto Rahardja, Pasi Fränti
Pattern Recognition (2021) Vol. 115, pp. 107874-107874
Open Access | Times Cited: 122
Semi-supervised anomaly detection algorithms: A comparative summary and future research directions
Miryam Elizabeth Villa-Pérez, Miguel Á. Álvarez‐Carmona, Octavio Loyola‐González, et al.
Knowledge-Based Systems (2021) Vol. 218, pp. 106878-106878
Closed Access | Times Cited: 104
RN-SMOTE: Reduced Noise SMOTE based on DBSCAN for enhancing imbalanced data classification
Ahmed Arafa, Nawal El‐Fishawy, Mohammed Badawy, et al.
Journal of King Saud University - Computer and Information Sciences (2022) Vol. 34, Iss. 8, pp. 5059-5074
Open Access | Times Cited: 69
Zero-day attack detection: a systematic literature review
Rasheed Ahmad, Izzat Alsmadi, Wasim A. Al-Hamdani, et al.
Artificial Intelligence Review (2023) Vol. 56, Iss. 10, pp. 10733-10811
Closed Access | Times Cited: 55
Anomaly detection based on weighted fuzzy-rough density
Zhong Yuan, Baiyang Chen, Jia Liu, et al.
Applied Soft Computing (2023) Vol. 134, pp. 109995-109995
Closed Access | Times Cited: 50
Outlier detection using iterative adaptive mini-minimum spanning tree generation with applications on medical data
Jia Li, Jiangwei Li, Chenxu Wang, et al.
Frontiers in Physiology (2023) Vol. 14
Open Access | Times Cited: 43
MFGAD: Multi-fuzzy granules anomaly detection
Zhong Yuan, Hongmei Chen, Chuan Luo, et al.
Information Fusion (2023) Vol. 95, pp. 17-25
Closed Access | Times Cited: 41
Learning Neural Representations for Network Anomaly Detection
Van Loi Cao, Miguel Nicolau, James McDermott
IEEE Transactions on Cybernetics (2018) Vol. 49, Iss. 8, pp. 3074-3087
Closed Access | Times Cited: 156
Isolation‐based anomaly detection using nearest‐neighbor ensembles
Tharindu Bandaragoda, Kai Ming Ting, David Albrecht, et al.
Computational Intelligence (2018) Vol. 34, Iss. 4, pp. 968-998
Closed Access | Times Cited: 123
Assessment of Data Suitability for Machine Prognosis Using Maximum Mean Discrepancy
Xiaodong Jia, Ming Zhao, Di Yuan, et al.
IEEE Transactions on Industrial Electronics (2017) Vol. 65, Iss. 7, pp. 5872-5881
Closed Access | Times Cited: 109
Outlier detection
Jiawei Yang, Susanto Rahardja, Pasi Fränti
(2019)
Closed Access | Times Cited: 107
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