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 low similarity of
the different 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 function
anticlustering() implements exact and heuristic anticlustering
algorithms as described in Papenberg and Klau (2021;
<doi:10.1037/met0000301>). The exact algorithms require that the GNU
linear programming kit (<https://www.gnu.org/software/glpk/glpk.html>)
is available and the R package 'Rglpk'
(<https://cran.R-project.org/package=Rglpk>) is installed. A
bicriterion anticlustering method proposed by Brusco et al. (2020;
<doi:10.1111/bmsp.12186>) is available through the function
bicriterion_anticlustering(), kplus_anticlustering() implements the
k-plus anticlustering approach proposed by Papenberg (2023;
<doi:10.31234/osf.io/7jw6v>). 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: |
0.6.4 |
| Depends: |
R (≥ 3.6.0) |
| Imports: |
Matrix, RANN (≥ 2.6.0) |
| Suggests: |
knitr, Rglpk, rmarkdown, testthat |
| Published: |
2023-05-02 |
| 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) |
| 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 |
| NeedsCompilation: |
yes |
| SystemRequirements: |
The exact (anti)clustering algorithms require that
the GNU linear programming kit (GLPK library) is installed
(<http://www.gnu.org/software/glpk/>). Rendering the vignette
requires pandoc. |
| Citation: |
anticlust citation info |
| CRAN checks: |
anticlust results |
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