From sets of good redescriptions to good sets of redescriptions

Redescription mining aims at finding pairs of queries over data variables that describe roughly the same set of observations. These redescriptions can be used to obtain different views on the same set of entities. So far, redescription mining methods have aimed at listing all redescriptions supported by the data. Such an approach can result in many redundant redescriptions and hinder the user’s ability to understand the overall characteristics of the data. In this work, we present an approach to identify and remove the redundant redescriptions, that is, an approach to move from a set of good redescriptions to a good set of redescriptions. We measure the redundancy of a redescription using a framework inspired by the concept of subjective interestingness based on maximum entropy distributions as proposed by De Bie (Data Min Knowl Discov 23(3):407–446, 2011 ). Redescriptions, however, generate specific requirements on the framework, and our solution differs significantly from the existing ones. Notably, our approach can handle disjunctions and conjunctions in the queries, whereas the existing approaches are limited only to conjunctive queries. Our framework can also handle data with Boolean, nominal, or real-valued data, possibly containing missing values, making it applicable to a wide variety of data sets. Our experiments show that our framework can efficiently reduce the redundancy even on large data sets.