A density and grid based cluster centers determination clustering algorithm is proposed. The computational complexity of the clustering process is reduced by using the gridding dataset. Firstly, the dataset space is divided into grids with the same size, and the number of data objects that are contained in grid is defined as the value of the grid density. The nearest distance from one grid to another with higher density is defined as the value of grid distance. The cluster center grids can be found since these grids always have high density value and large distance value. Then, a density-based division approach is used to accomplish the task of clustering. Finally, a comprehensive comparison is carried out to examine the clustering accuracy and execution time between the proposed clustering algorithm and some classical algorithms. Experiment results show that the proposed algorithm can lead to a higher accuracy with less execution time.