Unsupervised multidimensional hierarchical clustering

A method for multidimensional hierarchical clustering that is invariant to monotonic transformations of the distance metric is presented. The method derives a tree of clusters organized according to the homogeneity of intracluster and interpoint distances. Higher levels correspond to coarser clusters. At any level the method can detect clusters of different densities, shapes and sizes. The number of clusters and the parameters for clustering are determined automatically and adaptively for a given data set which makes it unsupervised and non-parametric. The method is simple, noniterative and requires low computation. Results on various sample data sets are presented.