Interactive Discovery of Interesting Subgroup Sets

Although subgroup discovery aims to be a practical tool for exploratory data mining, its wider adoption is hampered by redundancy and the re-discovery of common knowledge. This can be remedied by parameter tuning and manual result filtering, but this requires considerable effort from the data analyst. In this paper we argue that it is essential to involve the user in the discovery process to solve these issues. To this end, we propose an interactive algorithm that allows a user to provide feedback during search, so that it is steered towards more interesting subgroups. Specifically, the algorithm exploits user feedback to guide a diverse beam search. The empirical evaluation and a case study demonstrate that uninteresting subgroups can be effectively eliminated from the results, and that the overall effort required to obtain interesting and diverse subgroup sets is reduced. This confirms that within-search interactivity can be useful for data analysis.