Hierarchical Clustering via Spreading Metrics

Aurko Roy, Sebastian Pokutta; 18(88):1−35, 2017.


We study the cost function for hierarchical clusterings introduced by (Dasgupta, 2016) where hierarchies are treated as first-class objects rather than deriving their cost from projections into flat clusters. It was also shown in (Dasgupta, 2016) that a top-down algorithm based on the uniform Sparsest Cut problem returns a hierarchical clustering of cost at most $O\left(\alpha_n \log n\right)$ times the cost of the optimal hierarchical clustering, where $\alpha_n$ is the approximation ratio of the Sparsest Cut subroutine used. Thus using the best known approximation algorithm for Sparsest Cut due to Arora-Rao- Vazirani, the top-down algorithm returns a hierarchical clustering of cost at most $O\left(\log^{3/2} n\right)$ times the cost of the optimal solution. We improve this by giving an $O(\log{n})$-approximation algorithm for this problem. Our main technical ingredients are a combinatorial characterization of ultrametrics induced by this cost function, deriving an Integer Linear Programming (ILP) formulation for this family of ultrametrics, and showing how to iteratively round an LP relaxation of this formulation by using the idea of sphere growing which has been extensively used in the context of graph partitioning. We also prove that our algorithm returns an $O(\log{n})$- approximate hierarchical clustering for a generalization of this cost function also studied in (Dasgupta, 2016). Experiments show that the hierarchies found by using the ILP formulation as well as our rounding algorithm often have better projections into flat clusters than the standard linkage based algorithms. We conclude with constant factor inapproximability results for this problem: 1) no polynomial size LP or SDP can achieve a constant factor approximation for this problem and 2) no polynomial time algorithm can achieve a constant factor approximation under the Small Set Expansion hypothesis.


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