A Survey of Popular R Packages for Cluster Analysis

Abby Flynt and Nema Dean
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Abby Flynt: Bucknell University
Nema Dean: University of Glasgow

Journal of Educational and Behavioral Statistics, 2016, vol. 41, issue 2, 205-225

Abstract: Cluster analysis is a set of statistical methods for discovering new group/class structure when exploring data sets. This article reviews the following popular libraries/commands in the R software language for applying different types of cluster analysis: from the stats library, the kmeans , and hclust functions; the mclust library; the poLCA library; and the clustMD library. The packages/functions cover a variety of cluster analysis methods for continuous data, categorical data, or a collection of the two. The contrasting methods in the different packages are briefly introduced, and basic usage of the functions is discussed. The use of the different methods is compared and contrasted and then illustrated on example data. In the discussion, links to information on other available libraries for different clustering methods and extensions beyond basic clustering methods are given. The code for the worked examples in Section 2 is available at http://www.stats.gla.ac.uk/∼nd29c/Software/ClusterReviewCode.R

Keywords: cluster analysis; R software language; k-means; hierarchical clustering; model-based clustering (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:41:y:2016:i:2:p:205-225

DOI: 10.3102/1076998616631743

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