Lipshitz distance and hierarchical clustering

This note discusses the Lipshits distance between two different metrics (or definite) dissimilarities on a set. Given a metric space a universal lower bound is established for the Lipshits distance between the original metric on M and the metric defined by any hierarchical classification tree on M. Finally it is shown that single link clustering attains this lower bound.