Mining Relevant Sequence Patterns with CP-based Framework

Amina Kemmar (1) , Willy Ugarte Rojas (2) , Samir Loudni (2) , Thierry Charnois (3) , Yahia Lebbah (1) , Patrice Boizumault (2) , Bruno Crémilleux (2)

1 LITIO - Laboratoire d'Informatique et Technologies de l'Information d'Oran
2 Equipe CODAG - Laboratoire GREYC - UMR6072
3 LIPN - Laboratoire d'Informatique de Paris-Nord

Sequential pattern mining under various constraints is a challenging data mining task. The paper provides a generic framework based on constraint programming to discover sequence patterns defined by constraints on local patterns (e.g., gap, regular expressions) or constraints on patterns involving combination of local patterns such as relevant subgroups and top-k patterns. This framework enables the user to mine in a declarative way both kinds of patterns. The solving step is done by exploiting the machinery of Constraint Programming. For complex patterns involving combination of local patterns, we improve the mining step by using dynamic CSP. Finally, we present two case studies in biomedical information extraction and stylistic analysis in linguistics.

Mots clés: Sequential mining; Constraint programming; subgroup patterns

Domaines: Intelligence artificielle [cs.AI]

Amina Kemmar, Willy Ugarte Rojas, Samir Loudni, Thierry Charnois, Yahia Lebbah, et al.. Mining Relevant Sequence Patterns with CP-based Framework. IEEE Int. Conf. on Tools with Artificial Intelligence (ICTAI 2014), 2014, Limassol,, Cyprus. pp.552-559. ⟨hal-01145905⟩