This study introduces a new methodology for clustering mobile devices to determine their geographical proximity without using direct geolocation data. Using the DBSCAN algorithm, we aim to identify significant spatial patterns while preserving user privacy. The dataset used includes information on device manufacturers and network interactions. Clustering was performed after data preprocessing, with DBSCAN effectively grouping devices in proximity. Our results show that this method successfully identifies clusters that reflect meaningful geographical relationships among the devices. The evaluation included calculating the average and maximum distances within the clusters, demonstrating the robustness of the method. Despite its effectiveness, the success of the method depends on the quality and completeness of the input data. Future research should explore additional data sources and refine used algorithms to enhance accuracy and efficiency.