In Model-Driven Engineering (MDE), model transformations are basic and primordial entities. An efficient way to assist the definition of these transformations consists in completely or partially learning them. MTBE (Model Transformation By-Example) is an approach that aims at learning a model transformation from a set of examples, i.e. pairs of transformation source and target models. To implement this approach, we use Formal Concept Analysis as a learning mechanism in order to extract executable rules. In this paper, we investigate two learning strategies. In the first strategy, transformation rules are learned independently from each example. Then we gather these rules into a single set of rules. In the second strategy, we learn the set of rules from all the examples. The comparison of the two strategies on the well-known transformation problem of class diagrams to relational schema showed that the rules obtained from the two strategies are interesting. Besides the first one produces rules which are more proper to their examples and apply well compared to the second one which builds more detailed rules but larger and more difficult to analyze and to apply.