ClustGeo : Classification Ascendante Hiérarchique (CAH) avec contraintes de proximitté géographique

CHAVENT, Marie; KUENTZ-SIMONET, Vanessa; LABENNE, Amaury; SARACCO, Jerome

Metadatos

Mostrar el registro completo del ítem

Licencia de uso del documento

CHAVENT, Marie
Quality control and dynamic reliability [CQFD]
Institut de Mathématiques de Bordeaux [IMB]

KUENTZ-SIMONET, Vanessa
Environnement, territoires et infrastructures [UR ETBX]

LABENNE, Amaury
Quality control and dynamic reliability [CQFD]
Environnement, territoires et infrastructures [UR ETBX]

Idioma

Communication dans un congrès

Este ítem está publicado en

47èmes Journées de Statistique de la SFdS, 2015-06-01, Lille.

Resumen en inglés

Hierarchical Ascendant Clustering (HAC) is a well-known method of individual clustering. This method aims to bring together individuals who are similar regarding to variables which describe them. But when individuals are geographical units, the user may wish geographically close individuals to be put in same clusters and that, without too much deteriorating the quality of the partition. The proposed ClustGeo method allows geographical constraints of proximity to be taken into account within the HAC. For that purpose, a new Ward homogeneity criterion based on two different matrices of distances is proposed.< Leer menos

Metadatos

Compartir este ítem

Licencia de uso del documento