One of the main issues in the process of Knowledge Discovery in Databases is the Mining of Association Rules. Although a great variety of pattern mining algorithms have been designed to this purpose, their main problems rely on in the large number of extracted rules, that need to be filtered in a post-processing step resulting in fewer but more interesting results. In this paper we suggest a new algorithm, that allows the user to explore the rules space locally and incrementally. The user interests and preferences are represented by means of the new proposed formalism - the Rule Schemas. The method has been successfully tested on the database provided by Nantes Habitat.