Abstract : The datasets describing objects with Boolean properties are binary relations, i.e., 0/1 matrices. In such a relation, a closed itemset a maximal subset of objects sharing the same maximal subset of properties. Efficiently extracting every closed itemset satisfying user-defined relevancy constraints has been extensively studied. Nevertheless, many datasets are n-ary relations, i.e., 0/1 tensors. Reducing their analysis to two dimensions is ignoring potentially interesting additional dimensions. Moreover, the presence of noise in most real-life datasets leads to the fragmentation of the patterns to discover. Generalizing the definition of a closed itemset to make it suit relations of higher arity and tolerate some noise is straightforward. On the contrary, generalizing their extraction is very hard. Our extractor browses the candidate pattern space in an original way that does not favor any dimension. This search can be guided by a very broad class of relevancy constraints the patterns must satisfy. In particular, this thesis studies constraints to specifically mine dynamic graphs. Our extractor is orders of magnitude faster than known competitors, though limited in their applications. Despite these results, such an exhaustive approach often cannot, in a reasonable time, list patterns tolerating much noise. In this case, complementing the extraction with a hierarchical agglomeration of the patterns allows to achieve one's aims.