A variational approach to semi-supervised clustering
Peng Li, Yiming Ying, Colin K. Campbell · 2009
Abstract. We present a Bayesian variational inference scheme for semisupervised clustering in which data is supplemented with side information in the form of common labels. There is no mutual exclusion of classes assumption and samples are represented as a combinatorial mixture over multiple clusters. We illustrate performance on six datasets and find a positive comparison against constrained K-means clustering. 1