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2007 NIPS 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.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Data Science & Analytics and Machine Learning
📈 Trend Setter — Clustering
🧭 Keyword Pioneer — coalescent
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning
🐣 Hot Topic Early Bird — hierarchical clustering

Authors

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

Topics

Artificial Intelligence > Bayesian & Probabilistic > Bayesian Learning Machine Learning > Core Methods > Clustering Data Science & Analytics > Applications > Clustering Machine Learning > Bayesian & Probabilistic > Bayesian Learning Machine Learning > Bayesian & Probabilistic > Bayesian Inference Machine Learning > Learning Types > Clustering

Keywords

bayesian clustering bayesian inference hierarchical clustering document clustering sequential monte carlo coalescent greedy inference bayesian agglomerative clustering kingmans coalescent phylolinguistics coalescent process greedy algorithm monte carlo method
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