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 |
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