Forming consensus clusters from multiple input clusterings can improve accuracy and robustness. Current clustering ensemble methods require specifying the number of consensus clust...
Pu Wang, Carlotta Domeniconi, Kathryn Blackmond La...
A nonparametric Bayesian approach to co-clustering ensembles is presented. Similar to clustering ensembles, coclustering ensembles combine various base co-clustering results to ob...
Pu Wang, Kathryn B. Laskey, Carlotta Domeniconi, M...
Cluster ensembles provide a framework for combining multiple base clusterings of a dataset to generate a stable and robust consensus clustering. There are important variants of th...
Clustering is a fundamental task in many vision applications. To date, most clustering algorithms work in a batch setting and training examples must be gathered in a large group b...
Semi-supervised learning (SSL), is classification where additional unlabeled data can be used to improve accuracy. Generative approaches are appealing in this situation, as a mode...