This study presents a novel approach in the realm of data profiling and pattern recognition by integrating Frequent Pattern Tree (FPTree) with behavioral profiling methodologies. The innovative method focuses on harnessing the structural advantages of FPTree while simultaneously incorporating behavioral characteristics to enhance profiling accuracy. Traditional data profiling often overlooks the dynamic behavioral patterns exhibited by subjects, which can be pivotal in understanding and predicting future actions. By embedding behavioral attributes into the FPTree framework, this method offers a more robust and comprehensive profiling tool that caters to diverse applications, including marketing, security, and personalized user experiences. Through extensive experimental validation, the proposed approach demonstrates significant improvements in profiling precision and adaptability compared to conventional techniques. These findings underline the potential of integrating behavioral insights with frequent pattern analysis, paving the way for more advanced data-driven profiling solutions.