In the first half this paper, we generalize the theory of layer points [4] to the more general context of v-hierarchical clusterings [5]. Layer points provide a compressed description of a hierarchical clustering by recording only the points where a cluster changes. For multi-parameter hierarchical clusterings we consider both a global notion of layer points and layer points in the direction of a single parameter. An interleaving of hierarchical clusterings of the same set induces an interleaving of global layer points. In the particular, we consider cases where a hierarchical clustering of a finite metric space, Y , is interleaved with a hierarchical clustering of some sample X ⊆ Y .In the second half, we focus on the hierarchical clustering π 0 L −,k (Y ) for some finite metric space Y . When X ⊆ Y satisfies certain conditions guaranteeing X is well dispersed in Y and the points of Y are dense around X, there is an interleaving of layer points for π 0 L −,k (Y ) and a truncated version of L −,0 (X) = V − (X). Under stronger conditions, this interleaving defines a retract from the layer points for π 0 L −,k (Y ) to the layer points for π 0 L −,0 (X).