Directions for representing and comparing hierarchies are discussed.

Clustering methods that are invariant under monotone dissimilarity transformations are analyzed.

Most recent theories and methods concerning such concepts as ultrametric, tree metric, Robinson matrix, pyramid, and weak hierarchy are presented.

A linear theory for binary hierarchy is proposed to allow decomposing the data entries, as well as covariances, by the clusters.