  Recent years have seen a shift from a pattern mining process that has users
define constraints before-hand, and sift through the results afterwards, to an
interactive one. This new framework depends on exploiting user feedback to
learn a quality function for patterns. Existing approaches have a weakness in
that they use static pre-defined low-level features, and attempt to learn
independent weights representing their importance to the user. As an
alternative, we propose to work with more complex features that are derived
directly from the pattern ranking imposed by the user. Learned weights are then
aggregated onto lower-level features and help to drive the quality function in
the right direction. We explore the effect of different parameter choices
experimentally and find that using higher-complexity features leads to the
selection of patterns that are better aligned with a hidden quality function
while not adding significantly to the run times of the method.
  Getting good user feedback requires to quickly present diverse patterns,
something that we achieve but pushing an existing diversity constraint into the
sampling component of the interactive mining system LetSip. Resulting patterns
allow in most cases to converge to a good solution more quickly.
  Combining the two improvements, finally, leads to an algorithm showing clear
advantages over the existing state-of-the-art.
