  Identifying differences between groups is one of the most important knowledge
discovery problems. The procedure, also known as contrast sets mining, is
applied in a wide range of areas like medicine, industry, or economics.
  In the paper we present RuleKit-CS, an algorithm for contrast set mining
based on separate and conquer - a well established heuristic for decision rule
induction. Multiple passes accompanied with an attribute penalization scheme
provide contrast sets describing same examples with different attributes,
distinguishing presented approach from the standard separate and conquer. The
algorithm was also generalized for regression and survival data allowing
identification of contrast sets whose label attribute/survival prognosis is
consistent with the label/prognosis for the predefined contrast groups. This
feature, not provided by the existing approaches, further extends the usability
of RuleKit-CS.
  Experiments on over 130 data sets from various areas and detailed analysis of
selected cases confirmed RuleKit-CS to be a useful tool for discovering
differences between defined groups. The algorithm was implemented as a part of
the RuleKit suite available at GitHub under GNU AGPL 3 licence
(https://github.com/adaa-polsl/RuleKit).
  Keywords: contrast sets, separate and conquer, regression, survival
