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 a sequential covering - a well established heuristic for decision rule induction. Multiple passes accompanied with an attribute penalization scheme allow generating contrast sets describing same examples with different attributes, unlike the standard sequential covering. The ability to identify contrast sets in regression and survival data sets, the feature not provided by the existing algorithms, further extends the usability of RuleKit-CS. Experiments on wide range of data sets confirmed RuleKit-CS to be a useful tool for discovering differences between defined groups. The algorithm is a part of the RuleKit suite available at GitHub under GNU AGPL 3 licence (https://github.com/adaa-polsl/RuleKit). Keywords: Contrast sets, Sequential covering, Rule induction, Regression, Survival, Knowledge discovery

本文介绍了一种基于分而治之的算法RuleKit-CS，可以用来发现医学、工业、经济等领域的差异，.algorithm可以推广到回归和生存数据，进一步扩展了RuleKit-CS的可用性。

利用分治法策略对分类、回归和生存数据进行对比集的鲁棒挖掘