Rapid trajectory clustering based on neighbor spatial analysis

The existing trajectory clustering algorithms only use the position information in trajectory segmentation, which makes the selection of segment points unreliable. Meanwhile, the adopted distance metrics are originally designed to compare the whole trajectory, leads to the inaccuracy similarity of trajectory segments, and hence causes subsequent clustering risks. Moreover, the execution time of clustering significantly increases with the amount of data. To address these issues, the direction of velocity is introduced for improving the discrimination of segment points. Next, the shared nearest neighbor (SNN) similarity and Trajectory-Hausdorff distance are combined to construct the similarity matrix for overcoming the limitations of existing distance measures. Then, based on the R-tree index strategy, the neighbored trajectory segments are extracted and stored for fastening segment indexing. Finally, the Atlantic hurricane and elk datasets verify that the proposed algorithm can not only improve the clustering efficiency but also extract the trajectory model accurately.