Approximate Hierarchical Clustering via Sparsest Cut and Spreading Metrics

TL;DR

This paper analyzes approximation algorithms for hierarchical clustering based on sparsest cut and spreading metrics, providing new bounds, hardness results, and relaxations with potential for improved solutions.

Contribution

It proves that sparsest cut heuristics achieve an O(alpha_n) approximation, introduces a spreading metric SDP relaxation with an O(sqrt{log n}) gap, and establishes hardness results under the SSE hypothesis.

Findings

Sparsest cut heuristic achieves an O(alpha_n) approximation for hierarchical clustering.

Spreading metric SDP relaxation has an integrality gap of at most O(sqrt{log n}).

Hierarchical clustering is hard to approximate within any constant factor under SSE hypothesis.

Abstract

Dasgupta recently introduced a cost function for the hierarchical clustering of a set of points given pairwise similarities between them. He showed that this function is NP-hard to optimize, but a top-down recursive partitioning heuristic based on an alpha_n-approximation algorithm for uniform sparsest cut gives an approximation of O(alpha_n log n) (the current best algorithm has alpha_n=O(sqrt{log n})). We show that the aforementioned sparsest cut heuristic in fact obtains an O(alpha_n)-approximation for hierarchical clustering. The algorithm also applies to a generalized cost function studied by Dasgupta. Moreover, we obtain a strong inapproximability result, showing that the hierarchical clustering objective is hard to approximate to within any constant factor assuming the Small-Set Expansion (SSE) Hypothesis. Finally, we discuss approximation algorithms based on convex relaxations. We present a spreading metric SDP relaxation for the problem and show that it has integrality gap at most O(sqrt{log n}). The advantage of the SDP relative to the sparsest cut heuristic is that it provides an explicit lower bound on the optimal solution and could potentially yield an even better approximation for hierarchical clustering. In fact our analysis of this SDP served as the inspiration for our improved analysis of the sparsest cut heuristic. We also show that a spreading metric LP relaxation gives an O(log n)-approximation.