As a main tool of spatial data mining, spatial point clustering does offer interesting methods to address data effectively. At present, the study of spatial point clustering is mature and current methods may divide the initial data into different clusters. However, few methods consider geographi-cal features by linear distribution. Here, a novel spatial point clustering using the rolling circle (SPCURC) is proposed, which derives from the rolling sphere method. SPCURC uses a circle with a known radius is used to roll from the initial point to another point. The rolling does not stop until the condition is met. Then, a polygon cluster or a linear cluster will be generated with points contacted by the rolling circle.This paper also introduces the theory, the detailed calculation procedure and the algorithm complexity. In order to verify the proposed method, the simulated and actual experiments have been performed respectively. DBSCAN algorithm and hierarchical clustering method were employed as the comparative clustering methods. With two synthetic data sets, simulated experiments show that SPCURC is superior to the comparative methods in acquiring linear clusters and can get different types of clusters no matter whether the areas are low-density or high-density. With two real datasets, which contain a residential area in the south and some global seismic data, the actual experiments confirm that SPCURC has the advantages and the applicability to find the clusters that are in a linear way by comparison to DBSCAN and hierarchical clustering. The results indicate that the proposed algorithm is feasible, effective and practical providing the linear clusters and the clustering maps tallying with the initial data.