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The objective of this research is to extract triadic association rules from a triadic formal context
K := (K1, K2, K3, Y) where K1, K2 and K3 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 theirProducts from Suppliers. The algorithms and approach proposed have been
validated with experimentations on large real datasets.