"FAAD: an unsupervised fast and accurate anomaly detection method for a ..."

Bin Li et al. (2019)

> Home

Details and statistics

DOI: 10.1631/FITEE.1800038

access: closed

type: Journal Article

metadata version: 2025-01-19

- view
  - electronic edition via DOI
  - unpaywalled version
  authority control:
- export record
  dblp key:
  - journals/jzusc/LiWYLM19
- ask others
- share record
  persistent URL:
  - https://dblp.org/rec/journals/jzusc/LiWYLM19
Bin Li, Yijie Wang, Dongsheng Yang, Yongmou Li, Xingkong Ma:
FAAD: an unsupervised fast and accurate anomaly detection method for a multi-dimensional sequence over data stream. Frontiers Inf. Technol. Electron. Eng. 20(3): 388-404 (2019)

manage site settings

To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.