Abstract: In the real-world, constraints limits the spatial clustering must take into account the conditions of these restrictions, this paper studied the spatial clustering with obstacles. It mainly used the K-medoid algorithm to cluster, and it introduced an improved algorithm Guo Tao to solve the distance of spatial objects in the presence of obstacles. It is higher efficiency for small and medium-sized data. Through theoretical analysis and experimental, the algorithm is feasible.

Key words: Spatial clustering, Obstacle constrain, Evolutionary algorithm