Density based clustering techniques like DBSCAN can find arbitrary shaped clusters along with noisy outliers. A severe drawback of the method is its huge time requirement which makes it a unsuitable one for large data sets. One solution is to apply DBSCAN using only a few selected prototypes. But because of this the clustering result can deviate from that which uses the full data set. A novel method proposed in the paper is to use two types of prototypes, one at a coarser level meant to reduce the time requirement, and the other at a finer level meant to reduce the deviation of the result. Prototypes are derived using leaders clustering method. The proposed hybrid clustering method called l-DBSCAN is analyzed and experimentally compared with DBSCAN which shows that it could be a suitable one for large data sets.