Name
Abstract | ||
|---|---|---|
The development of mobile device technology and localization technology makes the collection of spatio-temporal information from moving objects much easier than before, and outlier detection for spatio-temporal trajectory is becoming increasingly attractive to data mining community. However, there is a lack of serious studies in this area. Several existing trajectory outlier methods such as the partition-and-detect framework can only deal with the trajectory data which only includes spatial attributes. It cannot be applied to the spatio-temporal trajectory data which includes both spatial and temporal attributes. In this paper, we propose an enhanced partition-and-detect framework to detect the outliers of spatio-temporal trajectory data. In this framework, we mainly introduce an outlier detection method which uses trajectory MBBs(Minimum Boundary Boxs). Based on this enhanced framework, we propose a congestion outlier detection method. Finally, the efficiency and accuracy are evaluated through experiments which use a real traffic dataset called US Highway 101 Dataset. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-24082-9_85 | ICHIT (1) |
Keywords | DocType | Citations |
outlier detection,data mining community,congestion outlier detection method,existing trajectory outlier method,novel outlier detection method,spatio-tempral trajectory data,enhanced partition-and-detect framework,spatio-temporal trajectory,spatio-temporal trajectory data,enhanced framework,trajectory data,trajectory mbbs | Conference | 0 |
PageRank | References | Authors |
0.34 | 11 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yan Li | 1 | 0 | 0.34 |
| Weonil Chung | 2 | 0 | 0.34 |
| Hae-Young Bae | 3 | 78 | 31.47 |