  In subgroup discovery, also known as supervised pattern mining, discovering
high quality one-dimensional subgroups and refinements of these is a crucial
task. For nominal attributes, this is relatively straightforward, as we can
consider individual attribute values as binary features. For numerical
attributes, the task is more challenging as individual numeric values are not
reliable statistics. Instead, we can consider combinations of adjacent values,
i.e. bins. Existing binning strategies, however, are not tailored for subgroup
discovery. That is, they do not directly optimize for the quality of subgroups,
therewith potentially degrading the mining result.
  To address this issue, we propose FLEXI. In short, with FLEXI we propose to
use optimal binning to find high quality binary features for both numeric and
ordinal attributes. We instantiate FLEXI with various quality measures and show
how to achieve efficiency accordingly. Experiments on both synthetic and
real-world data sets show that FLEXI outperforms state of the art with up to 25
times improvement in subgroup quality.
