  Mining itemsets that are the most interesting under a statistical model of
the underlying data is a commonly used and well-studied technique for
exploratory data analysis, with the most recent interestingness models
exhibiting state of the art performance. Continuing this highly promising line
of work, we propose the first, to the best of our knowledge, generative model
over itemsets, in the form of a Bayesian network, and an associated novel
measure of interestingness. Our model is able to efficiently infer interesting
itemsets directly from the transaction database using structural EM, in which
the E-step employs the greedy approximation to weighted set cover. Our approach
is theoretically simple, straightforward to implement, trivially parallelizable
and retrieves itemsets whose quality is comparable to, if not better than,
existing state of the art algorithms as we demonstrate on several real-world
datasets.
