# ClustGeo: an R package for hierarchical clustering with spatial constraints

2 CQFD - Quality control and dynamic reliability
IMB - Institut de Mathématiques de Bordeaux, Inria Bordeaux - Sud-Ouest
Abstract : 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.
Document type :
Journal articles
Domain :

https://hal.archives-ouvertes.fr/hal-01664018
Contributor : Marie Chavent <>
Submitted on : Tuesday, February 27, 2018 - 2:33:47 PM
Last modification on : Monday, March 5, 2018 - 1:13:08 AM

### Citation

Marie Chavent, Vanessa Kuentz-Simonet, Amaury Labenne, Jérôme Saracco. ClustGeo: an R package for hierarchical clustering with spatial constraints. Computational Statistics, Springer Verlag, In press, pp.1-24. 〈10.1007/s00180-018-0791-1〉. 〈hal-01664018〉

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