Name
Abstract | ||
|---|---|---|
Recent approaches for online clustering of moving objects location are restricted to instantaneous positions. Subse-quently, they fail to capture the behavior of moving objects over time. By continuously tracking sub-trajectories of moving object at each time window, it becomes possible to gain insight on the current behavior and potentially detect mobility patterns in real time. In our previous work [1], we proposed CUTiS, an incremental algorithm for discovering and maintaining the density-based clusters in trajectory data streams, while tracking the evolution of the clusters. This paper extends [1] to CUTiS* by proposing an indexing structure for sub-trajectory data based on a space-filling curve. The proposed index improves the performance of our approach without losing quality in the clusters results as we show in our experiments conducted on a real dataset. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2938503.2938516 | IDEAS |
Field | DocType | Citations |
Cluster (physics),Data mining,Data stream mining,Data stream clustering,Computer science,Data stream,Search engine indexing,Cluster analysis,Trajectory | Conference | 0 |
PageRank | References | Authors |
0.34 | 14 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| ticiana | 1 | 32 | 14.96 |
| Karine Zeitouni | 2 | 183 | 33.69 |
| José Antônio Fernandes de Macêdo | 3 | 465 | 51.40 |
| Marco A. Casanova | 4 | 1007 | 979.09 |