Self-organizing maps applied to clustering in MCDA

Alexandru Liviu Olteanu (1, 2) , Raymond Bisdorff (3) , Patrick Meyer (1, 2)

1 Lab-STICC_TB_CID_DECIDE
2 LUSSI - Département Logique des Usages, Sciences sociales et Sciences de l'Information
3 University of Luxembourg [Luxembourg]

In the field of Multi-Criteria Decision Aiding (MCDA) the problem of clustering, has received less attention than the classical problems. In this presentation, we continue our previous work on formally defining clustering in MCDA by extending the self-organizing maps of Kohonen to clustering in this context. When using measures of similarity, the K-means algorithm can in fact be identified as a particular case of the Kohonen maps, a case where the nodes in the map are completely disconnected from each other. We extend the Kohonen maps to clustering in MCDA, by firstly using measures from this domain in order to compare the alternatives together, and secondly proposing different map topologies in order to look for different types of structures over the clusters of alternatives.

Mots clés: Clustering; Self organizing maps; MCDA

Domaines: Recherche opérationnelle [math.OC]

Alexandru Liviu Olteanu, Raymond Bisdorff, Patrick Meyer. Self-organizing maps applied to clustering in MCDA. EWG-MCDA 2013 : 77th meeting of the EURO working group on multicriteria decision aiding, Apr 2013, Rouen, France. ⟨hal-00857306⟩