Approximating Hierarchical MV-sets for Hierarchical Clustering

Part of Advances in Neural Information Processing Systems 27 (NIPS 2014)

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Assaf Glazer, Omer Weissbrod, Michael Lindenbaum, Shaul Markovitch


The goal of hierarchical clustering is to construct a cluster tree, which can be viewed as the modal structure of a density. For this purpose, we use a convex optimization program that can efficiently estimate a family of hierarchical dense sets in high-dimensional distributions. We further extend existing graph-based methods to approximate the cluster tree of a distribution. By avoiding direct density estimation, our method is able to handle high-dimensional data more efficiently than existing density-based approaches. We present empirical results that demonstrate the superiority of our method over existing ones.