The 21st century witnessed the wide spread of smart phones such as iPhone. The daily growing importance of smart phones' also implies the increasing amount of user sensitive data stored in a cell phone, which positions mobile user authentication in an even more important role. Existing mobile user authentication methods either require special hardware or are not user transparent. In this paper, we present a mobile user authentication scheme using a data mining method that identifies a user based on cell phones' application history and GPS information. These data can be collected on almost every smart phone without user awareness and are prone to reflect a user's habit and biometric feature. We organize these data in directional graphs and introduce a metric based on which to classify the data. Experiments and results on real data are explained to show our scheme's effectiveness.