.png){kind=icon}
Return
Outlier detection
.png){kind=icon}
PRE
AI
DOI：10.1002/widm.19.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Outlier detection is an area of research with a long history which has applications in many fields. This article provides a nontechnical and concise overview of the commonly used approaches for detecting outliers, including classical methods, new challenges posed by real-world massive data, and some of the key advances made in recent years. (C) 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 261-268 DOI: 10.1002/widm.19
Keywords:
IDENTIFICATION
TESTS
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
11.7
Papers:
544
Citations:
5.3K
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSaveShareSave
ShareSaveShareSaveShareSaveShareSaveShareSave
View more