Spatial data clustering is one of the important data mining techniques for extracting knowledge from large amount of spatial data collected in various applications, such as remote sensing, GIS, computer cartography, environmental assessment and planning, etc. many useful spatial data clustering algorithms have been proposed in the past decade. DBSCAN is the most popular density clustering algorithm, which can discover clusters of any arbitrary shape and can handle the noise points effectively. However, DBSCAN can not discover clusters with large variance in density because it depends on globular value for its parameters Eps and MinPts which depend on each other. This paper presents an improved DBSCAN which can cluster spatial databases that contain clusters of different densities, shapes, and sizes effectively. Experimental results included to establish that the proposed improved DBSCAN is superior to DBSCAN in discovering clustering with large variance in density.