Pattern Discovery in Triadic Contexts

Many real-life applications are best represented as ternary and more generally n-ary relations. In this paper, we use Triadic Concept Analysis as a framework to mainly discover implications. Indeed, our contributions are as follows. First, we adapt the iPred algorithm for precedence link computation in concept lattices to the triadic framework. Then, new algorithms are proposed to compute triadic generators by extending the notion of faces and blockers to further calculate implications.