Special Issue on Unsupervised Anomaly Detection-Reference-Cited by-同舟云学术

Special Issue on Unsupervised Anomaly Detection

Published:2023-05-11 Issue:10 Volume:13 Page:5916
ISSN:2076-3417
Container-title:Applied Sciences
language:en
Short-container-title:Applied Sciences

Author:

Goldstein Markus¹^ORCID

Affiliation:

1. Department of Computer Science, Ulm University of Applied Sciences, 89075 Ulm, Germany

Abstract

Anomaly detection (also known as outlier detection) is the task of finding instances in a dataset which deviate markedly from the norm [...]

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Link

https://www.mdpi.com/2076-3417/13/10/5916/pdf

Reference12 articles.

1. Herskind Sejr, J., Christiansen, T., Dvinge, N., Hougesen, D., Schneider-Kamp, P., and Zimek, A. (2021). Outlier Detection with Explanations on Music Streaming Data: A Case Study with Danmark Music Group Ltd. Appl. Sci., 11.

2. Qin, F., Feng, H., Yang, T., and Hu, B. (2021). Low-Cost Active Anomaly Detection with Switching Latency. Appl. Sci., 11.

3. Shang, Z., Zhang, Y., Zhang, X., Zhao, Y., Cao, Z., and Wang, X. (2021). Time Series Anomaly Detection for KPIs Based on Correlation Analysis and HMM. Appl. Sci., 11.

4. Jiang, J.R., Kao, J.B., and Li, Y.L. (2021). Semi-Supervised Time Series Anomaly Detection Based on Statistics and Deep Learning. Appl. Sci., 11.

5. Al-amri, R., Murugesan, R.K., Man, M., Abdulateef, A.F., Al-Sharafi, M.A., and Alkahtani, A.A. (2021). A Review of Machine Learning and Deep Learning Techniques for Anomaly Detection in IoT Data. Appl. Sci., 11.

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