  The task of redescription mining explores ways to re-describe different
subsets of entities contained in a dataset and to reveal non-trivial
associations between different subsets of attributes, called views. This
interesting and challenging task is encountered in different scientific fields,
and is addressed by a number of approaches that obtain redescriptions and allow
for the exploration and analyses of attribute associations. The main limitation
of existing approaches to this task is their inability to use more than two
views. Our work alleviates this drawback. We present a memory efficient,
extensible multi-view redescription mining framework that can be used to relate
multiple, i.e. more than two views, disjoint sets of attributes describing one
set of entities. The framework can use any multi-target regression or
multi-label classification algorithm, with models that can be represented as
sets of rules, to generate redescriptions. Multi-view redescriptions are built
using incremental view-extending heuristic from initially created two-view
redescriptions. In this work, we use different types of Predictive Clustering
trees algorithms (regular, extra, with random output selection) and the Random
Forest thereof in order to improve the quality of final redescription sets
and/or execution time needed to generate them. We provide multiple performance
analyses of the proposed framework and compare it against the naive approach to
multi-view redescription mining. We demonstrate the usefulness of the proposed
multi-view extension on several datasets, including a use-case on understanding
of machine learning models - a topic of growing importance in machine learning
and artificial intelligence in general.
