A pattern-growth approach for mining trajectories

Global Positionning System (GPS) trajectory is an ordered list of GPS points, which are approximate since they depend on the quality of the GPS sensor and the covering satellites. Finding common frequent sub-trajectories in a given trajectories database enables to detect what are the most used paths encapsulating the objects behaviours. Most trajectories mining algorithms proposed in the literature require a preprocessing discretization step where the plan is discretized into tile blocks, enabling to use classical sequential mining algorithms. However, this step is time consuming and improper for real time applications. In this paper, we propose an algorithm, named TrajGrowth, which directly works on the raw data, without any preprocessing step and without requiring a laborious parameter setting for its execution. Clearly, instead the costly discretization step of standard approaches, we used a precision parameter for which low values push down the mining process to find more precise patterns. The experimental results show that our proposed approach is more precise than the discretization based approaches with a better processing time and avoiding redundant patterns.