Dependent Nonparametric Trees for Dynamic Hierarchical Clustering
Abstract
Hierarchical clustering methods offer an intuitive and powerful way to model a wide variety of data sets. However, the assumption of a fixed hierarchy is often overly restrictive when working with data generated over a period of time: We expect both the structure of our hierarchy, and the parameters of the clusters, to evolve with time. In this paper, we present a distribution over collections of time-dependent, infinite-dimensional trees that can be used to model evolving hierarchies, and present an efficient and scalable algorithm for performing approximate inference in such a model. We demonstrate the efficacy of our model and inference algorithm on both synthetic data and real-world document corpora.
Cite
Text
Dubey et al. "Dependent Nonparametric Trees for Dynamic Hierarchical Clustering." Neural Information Processing Systems, 2014.Markdown
[Dubey et al. "Dependent Nonparametric Trees for Dynamic Hierarchical Clustering." Neural Information Processing Systems, 2014.](https://mlanthology.org/neurips/2014/dubey2014neurips-dependent/)BibTeX
@inproceedings{dubey2014neurips-dependent,
title = {{Dependent Nonparametric Trees for Dynamic Hierarchical Clustering}},
author = {Dubey, Kumar Avinava and Ho, Qirong and Williamson, Sinead A and Xing, Eric P},
booktitle = {Neural Information Processing Systems},
year = {2014},
pages = {1152-1160},
url = {https://mlanthology.org/neurips/2014/dubey2014neurips-dependent/}
}