This paper presents a clustering algorithm for dot patterns in n-dimensional space. The n-dimensional space often represents a multivariate (nf-dimensional) function in a ns-dimensional space (ns+nf=n). The proposed algorithm decomposes the clustering problem into the two lower dimensional problems. Clustering in n f-dimensional space is performed to detect the sets of dots in n-dimensional space having similar nf-variate function values (location based clustering using a homogeneity model). Clustering in n s dimensional space is performed to detect the sets of dots in n-dimensional space having similar interneighbor distances (density based clustering with a uniformity model). Clusters in the n-dimensional space are obtained by combining the results in the two subspaces.