This talk presents recent advances in data mining, focusing on subgroup discovery and the ways to use subgroup discovery to generate actionable knowledge for decision support. Actionable knowledge is explicit symbolic knowledge, typically presented in the form of rules, that allow the decision maker to recognize some important relations needed to perform an appropriate action, such as planning a population screening campaign aimed at detecting individuals with high disease risk. Different subgroup discovery approaches are outlined, illustrated with case studies from medicine and functional genomics.