Check-in logs record how users access certain facilities. Discovering users' behavior patterns via logs has a wide range of applications, such as targeted advertising, criminal activity detection, etc. However, the discovery process is complex and challenging, due to the following reasons. (1) Log data is usually of long-term and contains noise, with sparse distribution of data in high-dimensional space. (2) Behavior patterns always relate to different time scales. (3) The variety of parameter selections and methods of data processing make traditional machine learning approaches difficult to obtain credible and understandable behavior analysis results. This study proposes an interactive approach to exploring behavior patterns from check-in logs. The process uses a dynamic subspace strategy which changes the time slices to analyze similar behavior patterns dynamically. The strategy reduces the effect of setting parameters artificially on the analytical results. The proposed approach integrates a visual analytical tool to support the process. Through visualization, analysts could understand the patterns found in each step-in real time, adjust the analysis process, comprehend and verify the results intuitively. The paper also presents a case study based on a real data set and a review of experts from different fields. The results confirm the effectiveness of the approach.