Discovering Emerging Graph Patterns from Chemicals

Guillaume Poezevara (1) , Bertrand Cuissart (1) , Bruno Crémilleux (1)

1 Equipe CODAG - Laboratoire GREYC - UMR6072

Emerging patterns are patterns of a great interest for characterizing classes. This task remains a challenge, especially with graph data. In this paper, we propose a method to mine the whole set of frequent emerging graph patterns, given a frequency threshold and an emergence threshold. Our results are achieved thanks to a change of the description of the initial problem so that we are able to design a process combining efficient algorithmic and data mining methods. Experiments on a real-world database composed of chemicals show the feasibility and the efficiency of our approach.

Mots clés: Data mining; emerging patterns; subgraph isomorphism; chemical information

Domaines: Intelligence artificielle [cs.AI]

Guillaume Poezevara, Bertrand Cuissart, Bruno Crémilleux. Discovering Emerging Graph Patterns from Chemicals. 18th International Symposium on Methodologies for Intelligent Systems (ISMIS'09), 2009, Prague, Czech Republic, France. pp.45--55. ⟨hal-01011298⟩