Abstract: Most existing outliers detection algorithms need to input parameters manually, can’t detect the global and local outliers at the same time, and can’t deal with such problems as uneven density data effectively. This paper proposed an outliers detection algorithm DD-DBSCAN based on density division. The main innovation includes: 1) Define a new concept of Cluster Density according to the method of Minimum Spanning Tree, the entered data is divided into many clusters of different density. It can handle the data of uneven distribution density; 2) Adopting the idea of “divide and rule”, detect outliers from the division data respectively, make the algorithm be able to deal with the global and local outliers at the same time; 3) It can calculate the parameter value for each cluster automatically, makes the algorithm needs no longer human input parameters (Clustering Radius (Eps) Etc). Experiments on 2D simulated data sets and Iris real data sets, compared with DBSCAN algorithm, the results show that the proposed algorithm has higher precision and accuracy.