Clustering problem is an unsupervised learning problem. It is a procedure that partition data objects into matching clusters. The data objects in the same cluster are quite similar to each other and dissimilar in the other clusters. The traditional algorithms do not meet the latest multiple requirements simultaneously for objects. Density-based clustering algorithms find clusters based on density of data points in a region. DBSCAN algorithm is one of the density-based clustering algorithms. It can discover clusters with arbitrary shapes and only requires two input parameters.In this paper, we propose a new algorithm based on DBSCAN. We design a new method for automatic parameters generation that create clusters with different densities and generates arbitrary shaped clusters. The kd-tree is used for increasing the memory efficiency. The performance of proposed algorithm is compared with DBSCAN. Experimental results indicate the superiority of proposed algorithm.