  This paper introduces hierarchical quasi-clustering methods, a generalization
of hierarchical clustering for asymmetric networks where the output structure
preserves the asymmetry of the input data. We show that this output structure
is equivalent to a finite quasi-ultrametric space and study admissibility with
respect to two desirable properties. We prove that a modified version of single
linkage is the only admissible quasi-clustering method. Moreover, we show
stability of the proposed method and we establish invariance properties
fulfilled by it. Algorithms are further developed and the value of
quasi-clustering analysis is illustrated with a study of internal migration
within United States.
