Rankcluster: An R package for clustering ...
Document type :
Article dans une revue scientifique
Title :
Rankcluster: An R package for clustering multivariate partial rankings
Author(s) :
Journal title :
The R Journal
Abbreviated title :
R j.
Volume number :
6
Pages :
10
Publisher :
R Foundation for Statistical Computing
Publication date :
2014-06-01
Keyword(s) :
Rankcluster
Model-based clustering
Multivariate ranking
Partial ranking
Model-based clustering
Multivariate ranking
Partial ranking
HAL domain(s) :
Mathématiques [math]/Statistiques [math.ST]
English abstract : [en]
Rankcluster is the first R package dedicated to ranking data. This package proposes modelling and clustering tools for ranking data, potentially multivariate and partial. Ranking data are modelled by the Insertion Sorting ...
Show more >Rankcluster is the first R package dedicated to ranking data. This package proposes modelling and clustering tools for ranking data, potentially multivariate and partial. Ranking data are modelled by the Insertion Sorting Rank (isr) model, which is a meaningful model parametrized by a central ranking and a dispersion parameter. A conditional independence assumption allows to take into account multivariate rankings, and clustering is performed by the mean of mixtures of multivariate isr model. The clusters parameters (central rankings and dispersion parameters) help the practitioners in the interpretation of the clustering. Moreover, the Rankcluster package provides an estimation of the missing ranking positions when rankings are partial. After an overview of the mixture of multivariate isr model, the Rankcluster package is described and its use is illustrated through two real datasets analysis.Show less >
Show more >Rankcluster is the first R package dedicated to ranking data. This package proposes modelling and clustering tools for ranking data, potentially multivariate and partial. Ranking data are modelled by the Insertion Sorting Rank (isr) model, which is a meaningful model parametrized by a central ranking and a dispersion parameter. A conditional independence assumption allows to take into account multivariate rankings, and clustering is performed by the mean of mixtures of multivariate isr model. The clusters parameters (central rankings and dispersion parameters) help the practitioners in the interpretation of the clustering. Moreover, the Rankcluster package provides an estimation of the missing ranking positions when rankings are partial. After an overview of the mixture of multivariate isr model, the Rankcluster package is described and its use is illustrated through two real datasets analysis.Show less >
Language :
Anglais
Audience :
Internationale
Popular science :
Non
Administrative institution(s) :
CNRS
Université de Lille
Université de Lille
Submission date :
2020-06-08T14:10:13Z
2020-06-09T08:57:37Z
2020-06-09T08:57:37Z
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