  We present a Dirichlet process mixture model over discrete incomplete
rankings and study two Gibbs sampling inference techniques for estimating
posterior clusterings. The first approach uses a slice sampling subcomponent
for estimating cluster parameters. The second approach marginalizes out several
cluster parameters by taking advantage of approximations to the conditional
posteriors. We empirically demonstrate (1) the effectiveness of this
approximation for improving convergence, (2) the benefits of the Dirichlet
process model over alternative clustering techniques for ranked data, and (3)
the applicability of the approach to exploring large realworld ranking
datasets.
