par Rosenfeld, Jean 
;De Smet, Yves ;Debeir, Olivier ;Decaestecker, Christine
Référence Pattern recognition, 114, 107850
Publication Publié, 2021-01-29
;De Smet, Yves ;Debeir, Olivier ;Decaestecker, Christine Référence Pattern recognition, 114, 107850
Publication Publié, 2021-01-29
Article révisé par les pairs
| Résumé : | This study considers the task of clustering for data characterized by peculiar quantitative features in that they express performance according to different indicators or criteria. Performance is supposed to be optimized in one way or the other, i.e. maximized or minimized. This peculiar type of data introduces a comparative context that is not generally taken into account in the field of pattern recognition, in general, and clustering, in particular. In the present study, we introduce different concepts and develop tools that facilitate the evaluation of data partitions in this comparative context leading to the consideration of asymmetric preference relationships between objects and between clusters. We show their usefulness on the basis of artificial data and also by analyzing the results produced on real data by means of clustering methods. |
