Clustering points in nD space through hierarchical structures

This article presents a technique for clustering points in nD space based on the concepts of irregular pyramids and minimum-distance classification. The structure we present consists of a number of levels. Each level consists of a number of clusters and each cluster contains one or more point nodes. The base of the structure is the set of input points (or feature vectors). The apex is a set of roots where every root is distant from every other root according to some proximity criteria.