Abstract. In this paper, we propose to adapt the batch version of selforganizing map (SOM) to background information in clustering task. It deals with constrained clustering with SOM in a deterministic paradigm. In this context we adapt the appropriate topological clustering to pairwise instance level constraints with the study of their informativeness and coherence properties for measuring their utility for the semi-supervised learning process. These measures will provide guidance in selecting the most useful constraint sets for the proposed algorithm. Experiments will be given over several databases for validating our approach in comparison with another constrained clustering ones.
Allab and Benabdeslem adapted topological clustering to pairwise constraints using a self-organizing map in a deterministic manner REF .
Constraint selection for semi-supervised topological clustering
Mathematics Computer Science