Based on the analysis of features of the grid-based clustering method-clustering in quest (CLIQUE) and density-based clustering method-density-based spatial clustering of applications with noise (DBSCAN), a new clustering algorithm named cooperative clustering based on grid and density (CLGRID) is presented. The new algorithm adopts an equivalent rule of regional inquiry and density unit identification. The central region of one class is calculated by the grid-based method and the margin region by a density-based method. By clustering in two phases and using only a small number of seed objects in representative units to expand the cluster, the frequency of region query can be decreased, and consequently the cost of time is reduced. The new algorithm retains positive features of both grid-based and density-based methods and avoids the difficulty of parameter searching. It can discover clusters of arbitrary shape with high efficiency and is not sensitive to noise. The application of CLGRID on test data sets demonstrates its validity and higher efficiency, which contrast with traditional DBSCAN with R* tree. Based on the analysis of features of the grid-based clustering method-clustering in quest (CLIQUE) and density-based clustering method-density-based spatial clustering of applications with noise (DBSCAN), a new clustering algorithm named cooperative clustering based on grid The new algorithm employs an equivalent rule of regional inquiry and density unit identification. The central region of one class is calculated by the grid-based method and the margin region by a density-based method. By clustering in two phases and using only a small number of seed objects in representative units to expand the cluster, the frequency of region query can be decreased, and consequently the cost of time is reduced. The new algorithm retains positive features of both grid-based and density-based methods and avoids the difficulty of parameter searching. It can discover clusters of arbitrary shape with high efficiency and is not sensitive to noise. The application of CLGR ID on test data sets demonstrates its validity and higher efficiency, which contrast with traditional DBSCAN with R * tree.