Name
Abstract | ||
|---|---|---|
Uncertain trajectories data present new challenges to trajectories data mining. This paper proposes a sketch-based trajectory clustering algorithm for uncertain trajectories. Based on the M-level Hilbert curve spatial partitioning, a candidate segments set is constructed to represent uncertain trajectories model precisely. For the large number of candidate segments, a sketch-based approach is used to create hash-compressed clusters. A sketch-based clustering algorithm is proposed to assignment the incoming uncertain trajectory to clusters. The experiments prove that the clustering algorithm has stable accuracy with variations of the sampling rate of trajectories data. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/FSKD.2012.6234171 | FSKD |
Keywords | Field | DocType |
uncertain trajectory,candidate segment,pattern clustering,sampling rate,sketch,hash-compressed clusters,m-level hilbert curve spatial partitioning,fractals,segments set,sketch-based uncertain trajectories clustering algorithm,set theory,uncertain trajectories data mining,clustering,data mining,hidden markov models,trajectory,accuracy,algorithm design and analysis,clustering algorithms,spatial partitioning | Space partitioning,Algorithm design,Correlation clustering,Pattern recognition,Computer science,Artificial intelligence,Cluster analysis,Hidden Markov model,Trajectory,Machine learning,Hilbert curve,Sketch | Conference |
Volume | Issue | ISBN |
null | null | 978-1-4673-0025-4 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jingyu Chen | 1 | 2 | 0.69 |
| Qiuyan Huo | 2 | 2 | 1.03 |
| Ping Chen | 3 | 109 | 19.57 |
| Xuezhou Xu | 4 | 3 | 1.73 |