Soft Threshold Constraints for Pattern Mining

Willy Ugarte Rojas (1) , Patrice Boizumault (1) , Samir Loudni (1) , Bruno Crémilleux (1)

1 Equipe CODAG - Laboratoire GREYC - UMR6072

Constraint-based pattern discovery is at the core of numerous data mining tasks. Patterns are extracted with respect to a given set of constraints (frequency, closedness, size, etc). In practice, many constraints require threshold values whose choice is often arbitrary. This difficulty is even harder when several thresholds are required and have to be combined. Moreover, patterns barely missing a threshold will not be extracted even if they may be relevant. In this paper, by using Constraint Programming we propose a method to integrate soft threshold constraints into the pattern discovery process. We show the relevance and the efficiency of our approach through a case study in chemoinformatics for discovering toxicophores.

Domaines: Intelligence artificielle [cs.AI]; Traitement du texte et du document; Apprentissage [cs.LG]

Willy Ugarte Rojas, Patrice Boizumault, Samir Loudni, Bruno Crémilleux. Soft Threshold Constraints for Pattern Mining. 15th Int. Conf. on Discovery Science (DS 2012), Oct 2012, Lyon, France. pp.313-327. ⟨hal-01023751⟩