We address the problem of discovering contexts that lead well-distinguished collections of individuals to change their pairwise agreement w.r.t. to their usual one. For instance, in the European parliament, while in overall, a strong disagreement is witnessed between deputies of the far-right French party Front National and deputies of the left party Front de Gauche, a strong agreement is observed between these deputies in votes related to the thematic: External relations with the union. We devise the method DSC (Discovering Similarities Changes) which relies on exceptional model mining to uncover three-set patterns that identify contexts and two collections of individuals where an unexpected strengthening or weakening of pairwise agreement is observed. In this presentation we are going first to introduce the concepts of pattern mining and Exceptional model mining. We will present afterward an overview of the aproach. We will discuss also the optimizations that allowed us to efficiently explore the search space taking advantage of the closure operators and the quality measure upperbounds. To conclude, we will show some results that we obtained over three real-world datasets and also some perspectives.
