Nonparametric Bayesian Clustering Ensembles
Abstract
Forming consensus clusters from multiple input clusterings can improve accuracy and robustness. Current clustering ensemble methods require specifying the number of consensus clusters. A poor choice can lead to under or over fitting. This paper proposes a nonparametric Bayesian clustering ensemble (NBCE) method, which can discover the number of clusters in the consensus clustering. Three inference methods are considered: collapsed Gibbs sampling, variational Bayesian inference, and collapsed variational Bayesian inference. Comparison of NBCE with several other algorithms demonstrates its versatility and superior stability.
Cite
Text
Wang et al. "Nonparametric Bayesian Clustering Ensembles." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2010. doi:10.1007/978-3-642-15939-8_28Markdown
[Wang et al. "Nonparametric Bayesian Clustering Ensembles." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2010.](https://mlanthology.org/ecmlpkdd/2010/wang2010ecmlpkdd-nonparametric/) doi:10.1007/978-3-642-15939-8_28BibTeX
@inproceedings{wang2010ecmlpkdd-nonparametric,
title = {{Nonparametric Bayesian Clustering Ensembles}},
author = {Wang, Pu and Domeniconi, Carlotta and Laskey, Kathryn Blackmond},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2010},
pages = {435-450},
doi = {10.1007/978-3-642-15939-8_28},
url = {https://mlanthology.org/ecmlpkdd/2010/wang2010ecmlpkdd-nonparametric/}
}