  Most of the world's digital data is currently encoded in a sequential form,
and compression methods for sequences have been studied extensively. However,
there are many types of non-sequential data for which good compression
techniques are still largely unexplored. This paper contributes insights and
concrete techniques for compressing various kinds of non-sequential data via
arithmetic coding, and derives re-usable probabilistic data models from fairly
generic structural assumptions. Near-optimal compression methods are described
for certain types of permutations, combinations and multisets; and the
conditions for optimality are made explicit for each method.
