  Assessing the quality of discovered results is an important open problem in
data mining. Such assessment is particularly vital when mining itemsets, since
commonly many of the discovered patterns can be easily explained by background
knowledge. The simplest approach to screen uninteresting patterns is to compare
the observed frequency against the independence model. Since the parameters for
the independence model are the column margins, we can view such screening as a
way of using the column margins as background knowledge.
  In this paper we study techniques for more flexible approaches for infusing
background knowledge. Namely, we show that we can efficiently use additional
knowledge such as row margins, lazarus counts, and bounds of ones. We
demonstrate that these statistics describe forms of data that occur in practice
and have been studied in data mining.
  To infuse the information efficiently we use a maximum entropy approach. In
its general setting, solving a maximum entropy model is infeasible, but we
demonstrate that for our setting it can be solved in polynomial time.
Experiments show that more sophisticated models fit the data better and that
using more information improves the frequency prediction of itemsets.
