Name
Abstract | ||
|---|---|---|
An outlier is an object that is considerably dissimilar with the remainder of the dataset. In this paper, we first propose the notion of centrality and center-proximity as novel outlierness measures which can be considered to represent the characteristics of all of the objects in the dataset. We then propose a graph-based outlier detection method which can solve the problems of local density, micro-cluster, and fringe objects. Finally, through extensive experiments, we show the effectiveness of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2396761.2398613 | CIKM |
Keywords | Field | DocType |
fringe object,novel outlierness measure,local density,extensive experiment,graph-based outlier detection method,centrality | Data mining,Local outlier factor,Graph,Anomaly detection,Pattern recognition,Computer science,Centrality,Remainder,Outlier,Artificial intelligence | Conference |
Citations | PageRank | References |
3 | 0.45 | 6 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Duck-Ho Bae | 1 | 79 | 9.31 |
| Seo Jeong | 2 | 3 | 0.79 |
| Sang-Wook Kim | 3 | 792 | 152.77 |
| Minsoo Lee | 4 | 315 | 31.33 |