Septembre 13, 2018, 2pm, salle des thèses de la faculté Jean Perrin

Abstract

In this thesis, We adress the well-known clustering and association rules mining problems. Our first contribution introduces a new clustering framework, where complex objects are described by proposi- tional formulas. First, we extend the two well-known k-means and hierarchical agglomerative clustering techniques to deal with these complex objects. Second, we introduce a new divisive algorithm for clus- tering objects represented explicitly by sets of models. Finally, we propose a propositional satisfiability based encoding of the problem of clustering propositional formulas without the need for an explicit representation of their models. In a second contribution, we propose a new propositional satisfiability based approach to mine association rules in a single step. The task is modeled as a propositional for- mula whose models correspond to the rules to be mined. To highlight the flexibility of our proposed framework, we also address other variants, namely the closed, minimal non-redundant, most general and indirect association rules mining tasks. Experiments on many datasets show that on the majority of the considered association rules mining tasks, our declarative approach achieves better performance than the state-of-the-art specialized techniques.

Keywords: Data mining, Clustering, Association rules, Propositional satisfiability.