Name
Abstract | ||
|---|---|---|
This paper proposes a new trajectory clustering scheme for objects moving on road networks. A trajectory on road networks can be defined as a sequence of road segments a moving object has passed by. We first propose a similarity measurement scheme that judges the degree of similarity by considering the total length of matched road segments. Then, we propose a new clustering algorithm based on such similarity measurement criteria by modifying and adjusting the FastMap and hierarchical clustering schemes. To evaluate the performance of the proposed clustering scheme, we also develop a trajectory generator considering the fact that most objects tend to move from the starting point to the destination point along their shortest path. The performance result shows that our scheme has the accuracy of over 95%. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/CIDM.2009.4938663 | CIDM |
Keywords | Field | DocType |
pattern clustering,trajectory clustering,hierarchical clustering schemes,fastmap,moving object,road network,similarity measurement scheme,road segment matching,road traffic,shortest path,noise,generators,trajectory,dsl,hierarchical clustering,titanium | Hierarchical clustering,Data mining,Fuzzy clustering,Shortest path problem,Correlation clustering,Computer science,Digital subscriber line,Trajectory clustering,Artificial intelligence,Cluster analysis,Trajectory,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4244-2765-9 | 21 | 0.84 |
References | Authors | |
17 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jung-Im Won | 1 | 86 | 10.56 |
| Sangwook Kim | 2 | 43 | 6.29 |
| Ji-haeng Baek | 3 | 21 | 0.84 |
| Junghoon Lee | 4 | 21 | 0.84 |