Maximum entropy models and subjective interestingness: an application to
  tiles in binary databases
  Recent research has highlighted the practical benefits of subjective
interestingness measures, which quantify the novelty or unexpectedness of a
pattern when contrasted with any prior information of the data miner
(Silberschatz and Tuzhilin, 1995; Geng and Hamilton, 2006). A key challenge
here is the formalization of this prior information in a way that lends itself
to the definition of an interestingness subjective measure that is both
meaningful and practical.
  In this paper, we outline a general strategy of how this could be achieved,
before working out the details for a use case that is important in its own
right.
  Our general strategy is based on considering prior information as constraints
on a probabilistic model representing the uncertainty about the data. More
specifically, we represent the prior information by the maximum entropy
(MaxEnt) distribution subject to these constraints. We briefly outline various
measures that could subsequently be used to contrast patterns with this MaxEnt
model, thus quantifying their subjective interestingness.
