The objective of this research is to extract triadic association rules from a triadic formal context K := (K 1, K 2, K 3, Y) where K 1, K 2 and K 3 respectively represent the sets of objects, properties (or attributes) and conditions while Y is a ternary relation between these sets. Our approach consists to define a procedure to map a set of dyadic association rules into a set of triadic ones. The advantage of the triadic rules compared to the dyadic ones is that they are less numerous and more compact than the second ones and convey a richer semantics of data. Our approach is illustrated through an example of ternary relation representing a set of Customers who purchase their Products from Suppliers. The algorithms and approach proposed have been validated with experimentations on large real datasets.