ClustGeo: an R package for hierarchical clustering with spatial constraints View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-12

AUTHORS

Marie Chavent, Vanessa Kuentz-Simonet, Amaury Labenne, Jérôme Saracco

ABSTRACT

In this paper, we propose a Ward-like hierarchical clustering algorithm including spatial/geographical constraints. Two dissimilarity matrices D0 and D1 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 D0 and the homogeneity criterion calculated with D1. 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. More... »

PAGES

1799-1822

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00180-018-0791-1

DOI

http://dx.doi.org/10.1007/s00180-018-0791-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1100554784


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