In this paper, we define a framework in order to deal with a broad class of data mining algorithms for database relation analysis: Functional dependency inference, minimal key inference, sampling database relations and testing normal forms. We point out that the common data centric step of these algorithms is the discovery of agree sets. Within this framework, we give a new characterization of left-hand sides of minimal functional dependencies from which two levelwise algorithms are devised for computing functional dependencies and minimal keys. We propose two approaches for discovering agree sets from database relations: The former is based on SQL queries while the latter makes use of a particular implementation technique based on stripped partitions. Experiments have been performed in order to compare the two approaches.