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A Variational Approach to Semi-Supervised
Clustering AbstractWe present a Bayesian variational inference scheme for semi- supervised 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 ¯nd a positive comparison against constrained K-means clustering.
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