Approximating Hierarchical MV-Sets for Hierarchical Clustering
Abstract
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.
Cite
Text
Glazer et al. "Approximating Hierarchical MV-Sets for Hierarchical Clustering." Neural Information Processing Systems, 2014.Markdown
[Glazer et al. "Approximating Hierarchical MV-Sets for Hierarchical Clustering." Neural Information Processing Systems, 2014.](https://mlanthology.org/neurips/2014/glazer2014neurips-approximating/)BibTeX
@inproceedings{glazer2014neurips-approximating,
title = {{Approximating Hierarchical MV-Sets for Hierarchical Clustering}},
author = {Glazer, Assaf and Weissbrod, Omer and Lindenbaum, Michael and Markovitch, Shaul},
booktitle = {Neural Information Processing Systems},
year = {2014},
pages = {999-1007},
url = {https://mlanthology.org/neurips/2014/glazer2014neurips-approximating/}
}