Fuzzy measures are powerful at modeling interactions between elements. Unfortunately, they use a number of coefficients that exponentially grows with the number of elements. Beyond the computational complexity, assigning a value to any coalition of a large set of elements does not make sense. k-order measures model interactions involving at most k elements. The number of coefficients to identify is reduced and their modeling capacity is preserved in real problems where the number of interacting elements is limited. In extreme situations of full redundancy or complementariness, it is mathematically proven that the complete fuzzy measure is both k-additive and k-maxitive. A learning algorithm to identify k-maxitive measures from labeled data is designed on the basis of HLMS (Heuristic Least Mean Squares). In a classification context, the study of synthetic data with partial redundancy or complementariness supports the idea that the difference between full and partial interaction is a matter of degree, not of kind. Dealing with two real world datasets, a comparison of the complete fuzzy measure and a k-maxitive one shows the number of interacting elements is limited and the k-maxitive measures yield the same characterization of interactions and a comparable classification accuracy.