2007
NeurIPS
NeurIPS 2007
Bayesian Agglomerative Clustering with Coalescents
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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Interdisciplinary Bridge
- Artificial Intelligence and Data Science & Analytics
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Trend Setter
- Markov Chain Monte Carlo
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Keyword Pioneer
- agglomerative clustering
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Hot Topic Early Bird
- greedy algorithm
Authors
Topics
Artificial Intelligence > Bayesian & Probabilistic > Bayesian Learning
Machine Learning > Core Methods > Clustering
Machine Learning > Optimization & Theory > Bayesian Inference
Data Science & Analytics > Applications > Clustering
Machine Learning > Bayesian & Probabilistic > Bayesian Learning
Artificial Intelligence > Bayesian & Probabilistic > Bayesian Inference
Machine Learning > Bayesian & Probabilistic > Markov Chain Monte Carlo
Keywords
bayesian clustering
bayesian inference
hierarchical clustering
document clustering
bayesian model
sequential monte carlo
coalescent
greedy inference
bayesian agglomerative clustering
kingmans coalescent
phylolinguistics
agglomerative clustering
coalescent process
greedy algorithm
monte carlo method
kingman's coalescent