  We propose a nearest neighbor based clustering algorithm that results in a
naturally defined hierarchy of clusters. In contrast to the agglomerative and
divisive hierarchical clustering algorithms, our approach is not dependent on
the iterative working of the algorithm, in the sense that the partitions of the
hierarchical clusters are purely defined in accordance with the input dataset.
Our method is a universal hierarchical clustering approach since it can be
implemented as bottom up or top down versions, both of which result in the same
clustering. We show that for certain types of datasets, our algorithm has
near-linear time and space complexity.
