DBSCAN (Density-Based Clustering of Applications with Noise )is one of the attractive algorithms among densitybased clustering algorithms. It characterized by its ability to detect clusters of various sizes and shapes with the presence of noise, but its performance degrades when data have different densities .In this paper, we proposed a new technique to separate data based on its density with a new samplingtechnique , the purpose of these new techniques is for getting data with homogenous

more »

... ty .The experimental results onsynthetic data and real world data show that the new technique enhanced the clustering of DBSCAN to large extent.