Automatic Number Plate Recognition (ANPR) data is easier to obtain than private Global Positioning System (GPS) data, and it contains more useful information, but the relatively mature GPS track data mining with vehicle group method did not apply to ANPR data, the existing accompanying vehicle group mining algorithm pays attention to the similarity of the trajectory and ignores the time factor when dealing with small amount of ANPR data. A clustering method based on trajectory feature to excavate the accompanying vehicle group was proposed. Aiming at the fact that the sampling points are fixed and the sampling time is uncertain in the ANPR data, whether two objects were accompanied was determined by the number of co-occurrence in the trajectory. The co-occurrence definition introduced the Hausdorff distance, taking into account the location, direction and time characteristics of the trajectory. The accompanying vehicle group with different but adjacent sampling points and similar trajectories was minned to improve the mining efficiency. The experimental results show that the proposed method is more effective than the existing method to excavate the vehicle group, and improves the efficiency by nearly two times when identifying the non-accompanying mode data.