  The problem of selecting a small, yet high quality subset of patterns from a
larger collection of itemsets has recently attracted lot of research. Here we
discuss an approach to this problem using the notion of decomposable families
of itemsets. Such itemset families define a probabilistic model for the data
from which the original collection of itemsets has been derived from.
Furthermore, they induce a special tree structure, called a junction tree,
familiar from the theory of Markov Random Fields. The method has several
advantages. The junction trees provide an intuitive representation of the
mining results. From the computational point of view, the model provides
leverage for problems that could be intractable using the entire collection of
itemsets. We provide an efficient algorithm to build decomposable itemset
families, and give an application example with frequency bound querying using
the model. Empirical results show that our algorithm yields high quality
results.
