  An exponential-time exact algorithm is provided for the task of clustering n
items of data into k clusters. Instead of seeking one partition, posterior
probabilities are computed for summary statistics: the number of clusters, and
pairwise co-occurrence. The method is based on subset convolution, and yields
the posterior distribution for the number of clusters in O(n * 3^n) operations,
or O(n^3 * 2^n) using fast subset convolution. Pairwise co-occurrence
probabilities are then obtained in O(n^3 * 2^n) operations. This is
considerably faster than exhaustive enumeration of all partitions.
