Pairwise Clustering and Graphical Models

Signi(cid:2)cant progress in clustering has been achieved by algorithms that are based on pairwise af(cid:2)nities between the datapoints. In particular, spectral clustering methods have the advantage of being able to divide arbitrarily shaped clusters and are based on ef(cid:2)cient eigenvector calcu- lations. However, spectral methods lack a straightforward probabilistic interpretation which makes it dif(cid:2)cult to automatically set parameters us- ing training data. In this paper we use the previously proposed typical cut framework for pairwise clustering. We show an equivalence between calculating the typical cut and inference in an undirected graphical model.