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.

本文介绍了一种关于主观趣味测量的普遍策略，通过将先前的信息作为对数据的不确定性的概率模型的约束来表示其先前信息，从而使之有意义并实用。同时，使用 MaxEnt 模型来衡量主观趣味测量的各种措施。