ClustGeo: an R package for hierarchical clustering with spatial constraints

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 α ∈ [ 0 , 1 ] . The dissimilarities can be non-Euclidean and the weights of the observations can be n...

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Bibliographic Details
Published inComputational statistics Vol. 33; no. 4; pp. 1799 - 1822
Main Authors Chavent, Marie, Kuentz-Simonet, Vanessa, Labenne, Amaury, Saracco, Jérôme
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2018
Springer Nature B.V
Springer Verlag
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Summary: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 α ∈ [ 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 α 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.
ISSN:0943-4062
1613-9658
DOI:10.1007/s00180-018-0791-1