Inclusion dependencies together with functional dependencies form the most fundamental data dependencies used in practice. They are respectively the generalization of foreign keys and keys. Their utility is important for all applications in which data semantic is important: For example to perform evolution or maintenance of existing databases, or to construct a data warehouse from production databases. In this paper we propose a levelwise algorithm to discover inclusion dependencies holding in a database. We use an existing framework, in which we have made the following contributions: an original algorithm to generate level i+1 candidates from level i IND, a coherent method to generate candidate INDs for level 1, an implementation of the proposed algorithm and experimental results on real-life database. Despite the inherent complexity of this problem, performance evaluations show the feasibility of our proposal.