Bayesian Agglomerative Clustering with Coalescents

Yee W. Teh, Hal Daume Iii, Daniel M. Roy

NeurIPS 2007 pp. 1473-1480

/neurips/2007/teh2007neurips-bayesian/

Abstract

We introduce a new Bayesian model for hierarchical clustering based on a prior over trees called Kingman’s coalescent. We develop novel greedy and sequential Monte Carlo inferences which operate in a bottom-up agglomerative fashion. We show experimentally the superiority of our algorithms over the state-of-the-art, and demonstrate our approach in document clustering and phylolinguistics.

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Cite

Text

Teh et al. "Bayesian Agglomerative Clustering with Coalescents." Neural Information Processing Systems, 2007.

Markdown

[Teh et al. "Bayesian Agglomerative Clustering with Coalescents." Neural Information Processing Systems, 2007.](https://mlanthology.org/neurips/2007/teh2007neurips-bayesian/)

BibTeX

@inproceedings{teh2007neurips-bayesian,
  title     = {{Bayesian Agglomerative Clustering with Coalescents}},
  author    = {Teh, Yee W. and Iii, Hal Daume and Roy, Daniel M.},
  booktitle = {Neural Information Processing Systems},
  year      = {2007},
  pages     = {1473-1480},
  url       = {https://mlanthology.org/neurips/2007/teh2007neurips-bayesian/}
}