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ClustGeo: an R package for hierarchical clustering with spatial constraints

hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
hal.structure.identifierQuality control and dynamic reliability [CQFD]
dc.contributor.authorCHAVENT, Marie
hal.structure.identifierEnvironnement, territoires et infrastructures [UR ETBX]
dc.contributor.authorKUENTZ-SIMONET, Vanessa
hal.structure.identifierEnvironnement, territoires et infrastructures [UR ETBX]
dc.contributor.authorLABENNE, Amaury
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
hal.structure.identifierQuality control and dynamic reliability [CQFD]
hal.structure.identifierEcole Nationale Supérieure de Cognitique [ENSC]
dc.contributor.authorSARACCO, Jérôme
dc.date.accessioned2024-04-04T02:54:42Z
dc.date.available2024-04-04T02:54:42Z
dc.date.issued2018-01-20
dc.identifier.issn0943-4062
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/192295
dc.description.abstractEnIn 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.
dc.language.isoen
dc.publisherSpringer Verlag
dc.title.enClustGeo: an R package for hierarchical clustering with spatial constraints
dc.typeArticle de revue
dc.identifier.doi10.1007/s00180-018-0791-1
dc.subject.halStatistiques [stat]/Calcul [stat.CO]
dc.subject.halStatistiques [stat]/Autres [stat.ML]
dc.identifier.arxiv1707.03897
bordeaux.journalComputational Statistics
bordeaux.page1-24
bordeaux.volume33
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.issue4
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.peerReviewedoui
hal.identifierhal-01664018
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-01664018v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Computational%20Statistics&rft.date=2018-01-20&rft.volume=33&rft.issue=4&rft.spage=1-24&rft.epage=1-24&rft.eissn=0943-4062&rft.issn=0943-4062&rft.au=CHAVENT,%20Marie&KUENTZ-SIMONET,%20Vanessa&LABENNE,%20Amaury&SARACCO,%20J%C3%A9r%C3%B4me&rft.genre=article


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