Part of Advances in Neural Information Processing Systems 37 (NeurIPS 2024) Main Conference Track
Mohammadhossein Bateni, Laxman Dhulipala, Willem Fletcher, Kishen N. Gowda, D Ellis Hershkowitz, Rajesh Jayaram, Jakub Lacki
We give an algorithm for Centroid-Linkage Hierarchical Agglomerative Clustering (HAC), which computes a $c$-approximate clustering in roughly $n^{1+O(1/c^2)}$ time. We obtain our result by combining a new centroid-linkage HAC algorithm with a novel fully dynamic data structure for nearest neighbor search which works under adaptive updates.We also evaluate our algorithm empirically. By leveraging a state-of-the-art nearest-neighbor search library, we obtain a fast and accurate centroid-linkage HAC algorithm. Compared to an existing state-of-the-art exact baseline, our implementation maintains the clustering quality while delivering up to a $36\times$ speedup due to performing fewer distance comparisons.