The chapter introduces the concept of Trajectory Pattern Mining, which involves analyzing spatial and temporal data to uncover patterns in object movements. The authors highlight the availability of data from sources like GPS and GSM towers and discuss potential applications such as predicting movement, aggregating movement behavior, discovering regions of interest, and identifying traffic flow and blockages. The curse of dimensionality is addressed, emphasizing the exponential increase in search space as pattern length increases. To mitigate this, the authors propose reducing dimensions through Regions of Interest (ROIs). They describe a step-wise heuristic for T-Pattern mining, where any frequent T-Pattern of length n+1 is an extension of a frequent T-Pattern of length n. The process involves detecting ROIs, converting trajectories to ROI sequences, and finding sequences that meet the support condition. The chapter also outlines a detailed algorithm for static discovery of T-Patterns, including the detection of ROIs, projection of ROI sequences, and iterative refinement of configurations. The results section demonstrates the effectiveness of the method through various examples and metrics, concluding with a summary of the findings.The chapter introduces the concept of Trajectory Pattern Mining, which involves analyzing spatial and temporal data to uncover patterns in object movements. The authors highlight the availability of data from sources like GPS and GSM towers and discuss potential applications such as predicting movement, aggregating movement behavior, discovering regions of interest, and identifying traffic flow and blockages. The curse of dimensionality is addressed, emphasizing the exponential increase in search space as pattern length increases. To mitigate this, the authors propose reducing dimensions through Regions of Interest (ROIs). They describe a step-wise heuristic for T-Pattern mining, where any frequent T-Pattern of length n+1 is an extension of a frequent T-Pattern of length n. The process involves detecting ROIs, converting trajectories to ROI sequences, and finding sequences that meet the support condition. The chapter also outlines a detailed algorithm for static discovery of T-Patterns, including the detection of ROIs, projection of ROI sequences, and iterative refinement of configurations. The results section demonstrates the effectiveness of the method through various examples and metrics, concluding with a summary of the findings.