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In many applications, it becomes crucial to help users to access to a huge amount of data by clustering them in a small number of classes described at an appropriate level of abstraction. In this paper, we present an approach based on the use of two languages of description of classes for the automatic clustering of multi-valued data. The first language of classes has a high power of abstraction and guides the construction of a lattice of classes covering the whole set of the data. The second language, more expressive and more precise, is the basis for the refinement of a part of the lattice that the user wants to focus on.