Abstract. Cluster ensembles provide a framework for combining mul- tiple base clusterings of a dataset to generate a stable and robust consensus clustering.
Jan 11, 2011 · Cluster ensembles provide a framework for combining multiple base clusterings of a dataset into a single consolidated clustering. Compared to ...
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This paper proposes a Bayesian clustering ensemble Gaussian process (BCEGP) model suitable for clustering and prediction problems for large-scale traffic flow ...
Dec 18, 2013 · Cluster ensembles provide a framework for combining multiple base clusterings of a dataset to generate a stable and robust consensus ...
Cluster ensembles provide a framework for combining multiple base clusterings of a dataset to generate a stable and robust consensus clustering.
Jan 11, 2011 · Cluster ensembles provide a framework for combining multiple base clusterings of a dataset to generate a stable and robust consensus ...
Mar 27, 2023 · Bayesian cluster analysis offers substantial benefits over algorithmic approaches by providing not only point estimates but also uncertainty in the clustering ...
This paper proposes a nonparametric. Bayesian clustering ensemble (NBCE) method, which can discover the number of clusters in the consensus clustering. Three ...
May 15, 2023 · Bayesian cluster analysis offers substantial benefits over algorithmic approaches by providing not only point estimates but also uncertainty in the clustering ...
This paper proposes a nonparametric Bayesian clustering ensemble (NBCE) method, which can discover the number of clusters in the consensus clustering. Three ...