Reviews: Flattening a Hierarchical Clustering through Active Learning

This paper derives complexity results for active learning queries to hierarchical clustering. The result is a partition or "cut", c, of the cluster tree, where the "flat" clustering is defined by the clusters at the leaves of a subtree of nodes AB(c) that have the same root as the original cluster tree. Learning occurs by making pairwise judgments on items (leaf nodes). All pairwise judgments form a "ground truth" matrix \Sigma. Given consistency conditions, this is an equivalent way to represent a clustering.