We introduce in this paper an itemset mining approach to tackle the fault localization problem, which is one of the most difficult processes in software debug- ging. We formalize the problem of fault localization as finding the k best patterns satisfying a set of constraints modelling the most suspicious statements. We use a Constraint Programming (CP) approach to model and to solve our itemset based fault localization problem. Our approach consists of two steps: (i) mining top-k suspicious suites of statements; (ii) fault localization by processing top-k patterns. Experiments performed on standard benchmark programs show that our approach enables to pro- pose a more precise localization than a standard approach