Name
Abstract | ||
|---|---|---|
Low sampling-rate and uncertain features of trajectory data present new challenges to trajectories data mining. This paper proposed a relationship graph-based trajectory clustering algorithm for objects moving on road networks. By constructing an approximate minimum spanning tree of a trajectory, based on the spatial distance of candidate segments, a distance measurement scheme is presented to judge the degree of similarity. The relationship graphical model is adopted to represent the network-constrained trajectory data. A modified RepStream clustering algorithm is proposed to retain the stable relationship information. The experiments show that the clustering algorithm has superior accuracy in low sampling-rate and sampling error trajectories data. © 2011 IEEE. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/FSKD.2011.6019795 | FSKD |
Keywords | Field | DocType |
clustering,distance measurement,relationship graphical model,trajectory,hidden markov models,clustering algorithms,graphical models,data mining,sampling error,minimum spanning tree,accuracy,graphical model,hidden markov model | k-medians clustering,Hierarchical clustering,Fuzzy clustering,Canopy clustering algorithm,CURE data clustering algorithm,Data stream clustering,Correlation clustering,Pattern recognition,Computer science,Artificial intelligence,Cluster analysis,Machine learning | Conference |
Volume | Issue | Citations |
3 | null | 1 |
PageRank | References | Authors |
0.34 | 16 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jingyu Chen | 1 | 2 | 0.69 |
| Ping Chen | 2 | 1 | 0.34 |
| Qiuyan Huo | 3 | 2 | 1.03 |
| Xuezhou Xu | 4 | 3 | 1.73 |