Hierarchical Clustering for Euclidean Data
Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, PMLR 89:2721-2730, 2019.
Recent works on Hierarchical Clustering (HC), a well-studied problem in exploratory data analysis, have focused on optimizing various objective functions for this problem under arbitrary similarity measures. In this paper we take the first step and give novel scalable algorithms for this problem tailored to Euclidean data in R^d and under vector-based similarity measures, a prevalent model in several typical machine learning applications. We focus primarily on the popular Gaussian kernel and present our results through the lens of the objective introduced recently by [MW’17]. We show the approximation factor in [MW’17] can be improved for Euclidean data. We further demonstrate both theoretically and experimentally that our algorithms scale to very high dimension d, while outperforming average-linkage and showing competitive results against other less scalable approaches.