  Hierarchical and k-medoids clustering are deterministic clustering algorithms
based on pairwise distances. Using these same pairwise distances, we propose a
novel stochastic clustering method based on random partition distributions. We
call our method CaviarPD, for cluster analysis via random partition
distributions. CaviarPD first samples clusterings from a random partition
distribution and then finds the best cluster estimate based on these samples
using algorithms to minimize an expected loss. We compare CaviarPD with
hierarchical and k-medoids clustering through eight case studies. Cluster
estimates based on our method are competitive with those of hierarchical and
k-medoids clustering. They also do not require the subjective choice of the
linkage method necessary for hierarchical clustering. Furthermore, our
distribution-based procedure provides an intuitive graphical representation to
assess clustering uncertainty.
