Incremental Frequent Sub-trajectory Mining Based on Dual Division

Frequent pattern mining has been one of the most important data mining tasks, which is widely used in traffic management, animal migration and path prediction. In the view of the large scale, high dimension and real-time updating characteristics of trajectory data in free space, this paper presents an efficient framework for frequent sub-trajectory mining. Firstly, we divide the trajectory twice in order to smooth and reduce the trajectory effectively. Secondly, we expand the distance measure in two dimensions to three to improve the reliability of frequent sub-trajectory mining. Finally, we introduce an incremental clustering algorithm based on density, which greatly reduces the space-time consumption of frequent sub-trajectory mining in real-time updating database. Experimental results demonstrate that our framework improved the efficiency greatly and obtained more reliable results.

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