Trajectory clustering is a useful approach to analyze trajectory data. Numbers of methods have been proposed in this field, of which TRACLUS is a representative trajectory clustering algorithm based on the partitionandgroup framework. However, TRACLUS fails to find the optimal partitioning when trajectory points falling on both side of the center line are far away from the center line and is sensitive to the input parameters. Aiming at those vulnerabilities, a new trajectory clustering algorithm TCDP is proposed in this paper. TCDP is composed of two steps. In the first step, trajectories are partitioned into subtrajectories using the improved MDL partitioning algorithm which is more applicative and has higher precision than the partitioning algorithm used in TRACLUS. In the second step, a new subtrajectories algorithm is proposed. It is based on the idea that subtrajectory centers are surrounded by lower density subtrajectories and far from other subtrajectory centers. The new subtrajectories algorithm is robust to input factor. TCDP resolves the defects TRACLUS having. Meanwhile, the experiment shows TCDP performs better in quality of trajectory clustering.