Abstract: Finding clusters on the basis of density distribution is a traditional approach to discover clusters with arbitrary shape Some density based clustering algorithms such as DBSCAN, OPTICS, DENCLUE, CLIQUE, etc have been explored in recent researches A new approach is presented, which is based on the ordered subspaces to find clusters The key idea is to sort the subspaces according to their density, and set a new cluster if the subspace is larger than its neighbors Since the number of the subspaces is much less than that of the data, very large databases with high dimensional data sets can be processed with high efficiency A new method is also presented to project high dimensional data, and then some results of clustering with visualization are demonstrated