Uniformity and homogeneity-based hierarchical clustering

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 nf -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 nsdimensional 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.