The spatial data mining (SDM) is a process that extracts knowledge from large volumes of spatial data. It takes into account the spatial relationships between the data. To integrate these relations in the mining process, SDM uses two main approaches: Static approach that integrates spatial relationships in a preprocessing phase, and dynamic approach that takes into consideration the spatial relationship during the process. In this work, we are interested in this last approach. Our proposition consists on taking into consideration the spatial component in the similarity measure. We propose two similarity measures; $d_{Dyn1}$$, $$d_{Dyn2}$. We will use those distances on the main task of SDM, spatial clustering, particularly on K-means algorithm. Moreover, a comparaison between these two approaches and other methods of clustering will be given. The tests are conducted on Boston dataset with 590 objects.