Explanations for Itemset Mining by Constraint Programming: A Case Study Using ChEMBL Data

Maksim Koptelov (1) , Albrecht Zimmermann (1) , Patrice Boizumault (1) , Ronan Bureau (2) , Jean-Luc Lamotte (1)

1 Equipe CODAG - Laboratoire GREYC - UMR6072
2 CERMN - Centre d'Etudes et de Recherche sur le Médicament de Normandie

In sensitive applications, such as drug development, offering experts an explanation for why data mining operations arrive at certain results adds a very valuable facet. In this work we benefit from modelling the task as a Constraint Satisfaction Problem (CSP) twice: by adding multiple constraints to the mining process and by deriving pattern failure explanations. We illustrate experimentally how to apply our method on data originally retrieved from the ChEMBL database [14]. We also report some interesting dependencies discovered by our method which are not easy to observe when analysing data manually.

Mots clés: Itemset mining; Constraint programming; Explainable AI

Domaines: Intelligence artificielle [cs.AI]; Chemo-informatique

Maksim Koptelov, Albrecht Zimmermann, Patrice Boizumault, Ronan Bureau, Jean-Luc Lamotte. Explanations for Itemset Mining by Constraint Programming: A Case Study Using ChEMBL Data. 21st International Symposium on Intelligent Data Analysis (IDA 2023), Apr 2023, Louvain-la Neuve, Belgium. ⟨10.1007/978-3-031-30047-9_17⟩. ⟨hal-04071276⟩