Clustering is one of the unsupervised learning prob-lems.It is a procedure which partitions data objects into groups.Many algorithms could not overcome the problems of morpho-logy,overlapping and the large number of clusters at the same time.Many scientific communities have used the clustering al-gorithm from the perspective of density,which is one of the best methods in clustering.This study proposes a density-based spa-tial clustering of applications with noise (DBSCAN) algorithm based on the selected high-density areas by automatic fuzzy-DBSCAN (AFD) which works with the initialization of two para-meters.AFD,by using fuzzy and DBSCAN features,is modeled by the selection of high-density areas and generates two para-meters for merging and separating automatically.The two gene-rated parameters provide a state of sub-cluster rules in the Cartesian coordinate system for the dataset.The model over-comes the problems of clustering such as morphology,overlap-ping,and the number of clusters in a dataset simultaneously.In the experiments,all algorithms are performed on eight data sets with 30 times of running.Three of them are related to overlap-ping real datasets and the rest are morphologic and synthetic datasets.It is demonstrated that the AFD algorithm outperforms other recently developed clustering algorithms.