Name
Abstract | ||
|---|---|---|
As an important research direction in KDD field, outlier detection has been drawing much attention from different communities. In this paper, two novel algorithms LDBOD and LDBOD+ for outlier detection are proposed. Similar to LOF, they also aim to find local outliers. However, LDBOD/LDBOD+ detects local outliers from the viewpoint of local distribution, which is characterized through three proposed measurements, local-average-distance, local-density, and local-asymmetry-degree. Several experiments were conducted to demonstrate the advantages of LDBOD/LDBOD+ compared with LOF. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1016/j.patrec.2008.01.019 | Pattern Recognition Letters |
Keywords | Field | DocType |
outlier detector,outlier detection,detects local outlier,local distribution,important research direction,local outlier,different community,proposed measurement,kdd field,novel local distribution,symmetry | Local outlier factor,Anomaly detection,Pattern recognition,Outlier,Error detection and correction,Signal classification,Artificial intelligence,Detector,Mathematics | Journal |
Volume | Issue | ISSN |
29 | 7 | Pattern Recognition Letters |
Citations | PageRank | References |
7 | 0.51 | 11 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yong Zhang | 1 | 104 | 33.61 |
| Su Yang | 2 | 110 | 14.58 |
| Yuanyuan Wang | 3 | 498 | 82.58 |