.png){kind=icon}
.png){kind=icon}
Return
A novel outlier cluster detection algorithm without top-n parameter
.png){kind=icon}
PRE
AI
DOI：10.1016/j.knosys.2017.01.013.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Outlier detection is an important task in data mining with numerous applications, including credit card fraud detection, video surveillance, etc. Outlier detection has been widely focused and studied in recent years. The concept about outlier factor of object is extended to the case of cluster. Although many outlier detection algorithms have been proposed, most of them face the top-n problem, i.e., it is difficult to know how many points in a database are outliers. In this paper we propose a novel outlier-cluster detection algorithm called ROCF based on the concept of mutual neighbor graph and on the idea that the size of outlier clusters is usually much smaller than the normal clusters. ROCF can automatically figure out the outlier rate of a database and effectively detect the outliers and outlier clusters without top-n parameter. The formal analysis and experiments show that this method can achieve good performance in outlier detection. (C) 2017 Elsevier B.V. All rights reserved.
Keywords:
Outlier detection
Outlier clusters
Top-n problem
Mutual neighbor
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.