In the realm of interior design, professionals categorize space layouts based on the attributes and characteristics of furniture. Various furnishings exist, and tools like Rhino6 can autonomously produce numerous layout designs. However, while such automated layout software offers convenience, the generated designs often fail to adhere to established interior design principles, such as “doors should not directly face beds” or “bed headboards should not be positioned against windows.” This study delineates two distinct features for algorithmic analysis. The first pertains to the angle and distance between the bed's edge midpoint and the room's window, while the second relates to the angle and distance between the bed's edge midpoint and the door. Subsequent analysis of these features via diverse clustering algorithms revealed that the Gaussian mixture model outperforms its counterparts in clustering efficiency. Consequently, this model emerges as a viable approach for scrutinizing interior design rules.