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Exploiting Background Knowledge for Knowledge-Intensive Subgroup Discovery

In general, knowledge-intensive data mining methods exploit background knowledge to improve the quality of their results. Then, in knowledge-rich domains often the interestingness of the mined patterns can be increased significantly. 
 
In this paper we categorize several classes of background knowledge for subgroup discovery, and present how the necessary knowledge elements can be modelled. Furthermore, we show how subgroup discovery methods benefit from the utilization of background knowledge, and discuss its application in an incremental process-model. The context of our work is to identify interesting diagnostic patterns to supplement a medical documentation and consultation system. We provide a case study in the medical domain, using a case base from a realworld application.