Acyclic schemes have numerous applications in databases and in machine learning, such as improved design, more effi- cient storage, and increased performance for queries and ma- chine learning algorithms. Multivalued dependencies (MVDs) are the building blocks of acyclic schemes. The discovery from data of both MVDs and acyclic schemes is more chal- lenging than other forms of data dependencies, such as Func- tional Dependencies, because these dependencies do not hold on subsets of data, and because they are very sensitive to noise in the data; for example a single wrong or missing tuple may invalidate the schema. In this paper we present Mai- mon, a system for discovering approximate acyclic schemes and MVDs from data. We give a principled definition of ap- proximation, by using notions from information theory, then describe the two components of Maimon: mining for ap- proximate MVDs, then reconstructing acyclic schemes from approximate MVDs. We conduct an experimental evaluation of Maimon on 20 real-world datasets, and show that it can scale up to 1M rows, and up to 30 columns.