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Clustering spatial data is a well-known problem that has been extensively studied. Although many methods have been proposed in the literature, but few have handled the spatial constraints properly, which may have significant consequences on the effectiveness of the clustering. Taking into account these constraints during the clustering process is costly and the modeling of the constraints is paramount for good performance. In this paper, we investigate the problem of clustering in the presence of constraints such as physical obstacles and facilitator, and introduce a new approach named DBOF which model the obstacle using polygons, and model the facilitator using the especial graphical structure with nodes of crossing points, Both theory analysis and experimental results confirm that DBOF can cluster data objects efficiently while considering all physical constraints and its complexity is linear with the difficulty of constraints.