Generalized trajectory fuzzy clustering based on the multi-objective mixed function

Spatio-temporal trajectory clustering can extract behavior and moving pattern of object with the change of time and space by exploring similar trajectories. Most of trajectory clustering method can be achieved by expanding the traditional clustering algorithms. Considering the limitations of fitness and optimization of most clustering algorithms, especially for spatio-temporal trajectory data sets, this paper proposes a trajectory fuzzy clustering method based on multi-objective mixed function, which can simultaneously optimize multiple objective function such as FCM and XB when perform particle swarm optimization method. And we also propose a new coarse-grained DTW based on interpolation point for generalization trajectory data and improvement the performance of measure the similarity between trajectories. The experimental results, which implement on the synthetized trajectory data and real vehicle history data by employing the new clustering algorithm, and clustering validity evaluation and hot spots analysis show that the proposed method, which combines different objective functions with different optimization criteria and particle swarm algorithm, can effectively solve the clustering problem and produce better clustering results than the traditional