  We consider the problem of defining the significance of an itemset. We say
that the itemset is significant if we are surprised by its frequency when
compared to the frequencies of its sub-itemsets. In other words, we estimate
the frequency of the itemset from the frequencies of its sub-itemsets and
compute the deviation between the real value and the estimate. For the
estimation we use Maximum Entropy and for measuring the deviation we use
Kullback-Leibler divergence.
  A major advantage compared to the previous methods is that we are able to use
richer models whereas the previous approaches only measure the deviation from
the independence model.
  We show that our measure of significance goes to zero for derivable itemsets
and that we can use the rank as a statistical test. Our empirical results
demonstrate that for our real datasets the independence assumption is too
strong but applying more flexible models leads to good results.
