Name
Abstract | ||
|---|---|---|
After the local outlier factor was first proposed, there is a large family of local outlier detection approaches derived from it. Since the existing approaches only focus on the extent of overall separation between an object and its neighbors, and ignore the degree of dispersion between them, the precision of these approaches will be affected by various degrees in the scattered datasets. In additi... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACCESS.2018.2886197 | IEEE Access |
Keywords | Field | DocType |
Outlier detection,local outlier factor,neighborhood variance,rough clustering,scattered dataset | Local outlier factor,Anomaly detection,Data mining,Computer science,Outlier,Cluster analysis,Nearest neighbor search,Distributed computing | Journal |
Volume | ISSN | Citations |
7 | 2169-3536 | 1 |
PageRank | References | Authors |
0.36 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shubin Su | 1 | 5 | 3.16 |
| Limin Xiao | 2 | 107 | 28.51 |
| Li Ruan | 3 | 123 | 25.10 |
| Fei Gu | 4 | 4 | 2.13 |
| shupan li | 5 | 8 | 3.71 |
| Zhaokai Wang | 6 | 2 | 1.39 |
| Rongbin Xu | 7 | 37 | 10.01 |