Clustering, as a powerful data mining technique for discovering interesting data distributions and patterns in the underlying database, is used in many fields, such as statistical data analysis, pattern recognition, image processing, and other business applications. Density-based Spatial Clustering of Applications with Noise (DBSCAN) (Esteret al., 1996) is a good performance clustering method for dealing with spatial data although it leaves many problems to be solved. For example, DBSCAN requires a necessary user-specified threshold while its computation is extremely time-consuming by current method such as OPTICS, etc. (Ankerstet al., 1999), and the performance of DBSCAN under different norms has yet to be examined. In this paper, we first developed a method based on statistical information of distance space in database to determine the necessary threshold. Then our examination of the DBSCAN performance under different norms showed that there was determinable relation between them. Finally, we used two artificial databases to verify the effectiveness and efficiency of the proposed methods.