anticlust: Subset Partitioning via Anticlustering

The method of anticlustering partitions a pool of elements into groups (i.e., anticlusters) with the goal of maximizing between-group similarity or within-group heterogeneity. The anticlustering approach thereby reverses the logic of cluster analysis that strives for high within-group homogeneity and clear separation between groups. Computationally, anticlustering is accomplished by maximizing instead of minimizing a clustering objective function, such as the intra-cluster variance (used in k-means clustering) or the sum of pairwise distances within clusters. The main function anticlustering() gives access to optimal and heuristic anticlustering methods described in Papenberg and Klau (2021; <doi:10.1037/met0000301>), Brusco et al. (2020; <doi:10.1111/bmsp.12186>), and Papenberg (2024; <doi:10.1111/bmsp.12315>). The optimal algorithms require that an integer linear programming solver is installed. This package will install 'lpSolve' (<https://cran.r-project.org/package=lpSolve>) as a default solver, but it is also possible to use the package 'Rglpk' (<https://cran.r-project.org/package=Rglpk>), which requires the GNU linear programming kit (<https://www.gnu.org/software/glpk/glpk.html>), or the package 'Rsymphony' (<https://cran.r-project.org/package=Rsymphony>), which requires the SYMPHONY ILP solver (<https://github.com/coin-or/SYMPHONY>). 'Rglpk' and 'Rsymphony' have to be manually installed by the user because they are only "suggested" dependencies. Full access to the bicriterion anticlustering method proposed by Brusco et al. (2020) is given via the function bicriterion_anticlustering(), while kplus_anticlustering() implements the full functionality of the k-plus anticlustering approach proposed by Papenberg (2024). Some other functions are available to solve classical clustering problems. The function balanced_clustering() applies a cluster analysis under size constraints, i.e., creates equal-sized clusters. The function matching() can be used for (unrestricted, bipartite, or K-partite) matching. The function wce() can be used optimally solve the (weighted) cluster editing problem, also known as correlation clustering, clique partitioning problem or transitivity clustering.

Version:	0.8.7
Depends:	R (≥ 3.6.0)
Imports:	Matrix, RANN (≥ 2.6.0), lpSolve
Suggests:	knitr, palmerpenguins, Rglpk, rmarkdown, Rsymphony, tinytest
Published:	2024-10-01
DOI:	10.32614/CRAN.package.anticlust
Author:	Martin Papenberg [aut, cre], Meik Michalke [ctb] (centroid based clustering algorithm), Gunnar W. Klau [ths], Juliane V. Nagel [ctb] (package logo), Martin Breuer [ctb] (Bicriterion algorithm by Brusco et al.), Marie L. Schaper [ctb] (Example data set), Max Diekhoff [ctb] (Optimal maximum dispersion algorithm)
Maintainer:	Martin Papenberg <martin.papenberg at hhu.de>
BugReports:	https://github.com/m-Py/anticlust/issues
License:	MIT + file LICENSE
URL:	https://github.com/m-Py/anticlust, https://m-py.github.io/anticlust/
NeedsCompilation:	yes
SystemRequirements:	Rendering the vignette requires pandoc (<https://pandoc.org/>).
Citation:	anticlust citation info
CRAN checks:	anticlust results

Documentation:

Reference manual:	anticlust.pdf
Vignettes:	Some best practices for anticlustering (source, R code) Using categorical variables with anticlustering (source, R code) Speeding up anticlustering (source, R code) Using the R package anticlust for stimulus selection in experiments (source, R code)

Downloads:

Package source:	anticlust_0.8.7.tar.gz
Windows binaries:	r-devel: anticlust_0.8.7.zip, r-release: anticlust_0.8.7.zip, r-oldrel: anticlust_0.8.7.zip
macOS binaries:	r-release (arm64): anticlust_0.8.7.tgz, r-oldrel (arm64): anticlust_0.8.7.tgz, r-release (x86_64): anticlust_0.8.7.tgz, r-oldrel (x86_64): anticlust_0.8.7.tgz
Old sources:	anticlust archive

Reverse dependencies:

Reverse imports:

CEGO

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