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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 exact 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 exact algorithms require that an integer linear programming solver is installed, either the GNU linear programming kit (<https://www.gnu.org/software/glpk/glpk.html>) together with the interface package 'Rglpk' (<https://cran.R-project.org/package=Rglpk>), or the SYMPHONY ILP solver (<https://github.com/coin-or/SYMPHONY>) together with the interface package 'Rsymphony' (<https://cran.r-project.org/package=Rsymphony>). 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: | 0.8.5 |
Depends: | R (≥ 3.6.0) |
Imports: | Matrix, RANN (≥ 2.6.0) |
Suggests: | knitr, palmerpenguins, Rglpk, rmarkdown, Rsymphony, tinytest |
Published: | 2024-05-05 |
DOI: | 10.32614/CRAN.package.anticlust |
Author: | Martin Papenberg |
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: | The exact (anti)clustering algorithms require that either the GNU linear programming kit (GLPK library) is installed (<http://www.gnu.org/software/glpk/>) or the SYMPHONY open source MILP solver (<https://github.com/coin-or/SYMPHONY>). Rendering the vignette requires pandoc (<https://pandoc.org/>). |
Citation: | anticlust citation info |
CRAN checks: | anticlust results |
Package source: | anticlust_0.8.5.tar.gz |
Windows binaries: | r-devel: anticlust_0.8.5.zip, r-release: anticlust_0.8.5.zip, r-oldrel: anticlust_0.8.5.zip |
macOS binaries: | r-release (arm64): anticlust_0.8.5.tgz, r-oldrel (arm64): anticlust_0.8.5.tgz, r-release (x86_64): anticlust_0.8.5.tgz, r-oldrel (x86_64): anticlust_0.8.5.tgz |
Old sources: | anticlust archive |
Reverse imports: | CEGO |
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