We describe the internal algorithmics of our recent implementation of a closure-based self-tuning associator: yacaree. This system is designed so as not to request the user to specify any threshold. In order to avoid the need of a support threshold, we introduce an algorithm that constructs closed sets in order of decreasing support; we are not aware of any similar previous algorithm. In order not to overwhelm the user with large quantities of partial implications, our system filters the output according to a recently studied lattice-closure-based notion of con fidence boost, and self-adjusts the threshold for that rule quality measure as well. As a consequence, the necessary algorithmics interact in complicated ways. In order to control this interaction, we have resorted to a well-known, powerful conceptual tool, called Iterators: this notion allows us to distribute control among the various algorithms at play in a relatively simple manner, leading to a fully operative, open-source, effi cient system for discovery of partial implications in relational data.