Clustering in data mining is used for identifying useful patterns and interesting distributions in the underlying data. Several algorithms for clustering large data sets have been proposed in the literature using different techniques. Density-based method is one of these methodologies which can detect arbitrary shaped clusters where clusters are defined as dense regions separated by low density regions. In this paper, we present a new clustering algorithm to enhance the density-based algorithm DBSCAN. Synthetic datasets are used for experimental evaluation which shows that the new clustering algorithm is faster and more scalable than the original DBSCAN.