  Exemplar-based clustering methods have been shown to produce state-of-the-art
results on a number of synthetic and real-world clustering problems. They are
appealing because they offer computational benefits over latent-mean models and
can handle arbitrary pairwise similarity measures between data points. However,
when trying to recover underlying structure in clustering problems, tailored
similarity measures are often not enough; we also desire control over the
distribution of cluster sizes. Priors such as Dirichlet process priors allow
the number of clusters to be unspecified while expressing priors over data
partitions. To our knowledge, they have not been applied to exemplar-based
models. We show how to incorporate priors, including Dirichlet process priors,
into the recently introduced affinity propagation algorithm. We develop an
efficient maxproduct belief propagation algorithm for our new model and
demonstrate experimentally how the expanded range of clustering priors allows
us to better recover true clusterings in situations where we have some
information about the generating process.
