In track clustering research, traditional clustering algorithms use 3D information without considering time. They neglect the impact of aircraft speed and heading change on track clustering results. Furthermore, the outlier data caused by the airborne and ground equipment of secondary radar and signal masking are difficult to identify and eliminate, resulting in unsatisfactory clustering results. Therefore, this paper proposes a LOFC (LOF outlier detection and clustering-based method) algorithm, which involves time window segmentation and applies the average speed and heading change of arrival aircraft as the factors for determining the cluster size of the arrival tracks. The concepts of outlier detection and outlier eliminating rate are also introduced to recognize and clean outliers. The simulation results based on arrival radar data show that the proposed algorithm can effectively identify and remove outliers, and when the influence factor is 0.7, the curvature of the track reaches the minimum and the resulting center track becomes the most smooth. The algorithm is proved to be feasible and optimal for track clustering and outlier recognition and elimination.