Lightly-supervised clustering using pairwise constraint propagation

This paper focuses on providing a high-quality semi-supervised clustering with small quantities of constraints. A clustering method called CP-KMeans is proposed for propagating pairwise constraints to nearby instances using a Gaussian function. This method takes a few easily specified constraints, and propagates them to nearby pairs of points to constrain the local neighborhood. clustering with these propagated constraints can yield superior performance with fewer constraints than clustering with only the original user-specified constraints. The experimental results on several data sets show that CP-KMeans obtain high performance with fewer constraints compared with other two semi-supervised clustering algorithms.