Biomedical data useful for data mining are often distributed across multiple databases. These databases may be aggregated using several techniques to create single data sets that may be mined using standard approaches; however, separate databases may, in their design or data representation, capture information that is analytically useful and that is lost on integration. Recent techniques for mining multiple databases simultaneously but separately may preserve and leverage the unique perspectives within each database. This article presents an example, “dual mining,” in which concurrent analysis of a target database with a related knowledge base can improve the identification of association patterns in the target most likely to be of interest for further analysis.