Abstract
Relational Concept Analysis (RCA) is an extension to the Formal Concept Analysis (FCA) which is an unsupervised classification method producing concept lattices. In addition RCA considers relations between objects from different contexts that allow for the creation of links between lattices. This feature makes it more intuitive to extract knowledge from relational data and gives richer results. However, data with many relations imply scalability problems and results that are difficult to exploit. We propose in this article a possible adaptation of RCA to explore relations in a supervised way in order to increase the performance and the pertinence of the results.