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 (n/sub f/-dimensional) function in a n/sub s/-dimensional space (n/sub s/+n/sub f/=n). The proposed algorithm decomposes the clustering problem into the two lower dimensional problems. Clustering in n/sub f/-dimensional space is performed to detect the sets of dots in n-dimensional space having similar n/sub f/-variate function values (location based clustering using a homogeneity model). Clustering in n/sub 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.