The discovery of route patterns from trajectory data generated by moving objects is an essential problem for location-aware computing. However, the high degree of uncertainty of personal trajectory data significantly disturbs the existing route pattern mining approaches, and results in finding only short and incomplete patterns with high computational complexity. In this paper, we propose a personal trajectory data mining framework, which includes a group-and-partition trajectory abstraction technique and a frequent pattern mining algorithm called SCPM (Spatial Continuity based Pattern Mining). The group-and-partition technique can discover common sub-segments which are used to abstract the original trajectory data. The SCPM algorithm can efficiently derive longer and more complete route patterns from the abstracted personal trajectory data by tolerating various kinds of disturbances during the trips. Based on the real-world personal trajectory data, we conducted a number of experiments to evaluate the performance of our framework. The experimental results demonstrate that our framework is more efficient and effective as compared with the existing route pattern mining approaches, and the extracted route patterns can be effectively utilized to predict users¡¦ future route.