There are still very few systems performing a Similarity Based Learning and using a First Order Logic (FOL) representation. This limitation comes from the intrinsic complexity of the learning processes in FOL and from the difficulty to deal with numerical knowledge in this representation. In this paper, we show that major learning processes, namely generalization and clustering, can be solved in a homogeneous way by using a similarity measure. As this measure is defined, the similarity computation comes down to a problem of solving a set of equations in several unknowns. The representation language used to express our examples is a subset of FOL allowing to express both quantitative knowledge and a relevance scale on the predicates.