ClustGeo: an R package for hierarchical clustering with spatial constraints
CHAVENT, Marie
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]
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Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]
CHAVENT, Marie
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]
SARACCO, Jérôme
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]
Ecole Nationale Supérieure de Cognitique [ENSC]
< Réduire
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]
Ecole Nationale Supérieure de Cognitique [ENSC]
Langue
en
Article de revue
Ce document a été publié dans
Computational Statistics. 2018-01-20, vol. 33, n° 4, p. 1-24
Springer Verlag
Résumé en anglais
In this paper, we propose a Ward-like hierarchical clustering algorithm including spatial/geographical constraints. Two dissimilarity matrices $D_0$ and $D_1$ are inputted, along with a mixing parameter $\alpha \in [0,1]$. ...Lire la suite >
In this paper, we propose a Ward-like hierarchical clustering algorithm including spatial/geographical constraints. Two dissimilarity matrices $D_0$ and $D_1$ are inputted, along with a mixing parameter $\alpha \in [0,1]$. The dissimilarities can be non-Euclidean and the weights of the observations can be non-uniform. The first matrix gives the dissimilarities in the "feature space" and the second matrix gives the dissimilarities in the "constraint space". The criterion minimized at each stage is a convex combination of the homogeneity criterion calculated with $D_0$ and the homogeneity criterion calculated with $D_1$. The idea is then to determine a value of $\alpha$ which increases the spatial contiguity without deteriorating too much the quality of the solution based on the variables of interest i.e. those of the feature space. This procedure is illustrated on a real dataset using the R package ClustGeo.< Réduire
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