anticlust {anticlust} | R Documentation |

## anticlust: Subset Partitioning via Anticlustering

### Description

The method of anticlustering partitions a pool of elements into
groups (i.e., anticlusters) in such a way that the between-group
similarity is maximized and – at the same time – the within-group
heterogeneity is maximized. This 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 (2020; <doi:10.1037/met0000301>). The exact approach requires
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. 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.

### Primary functions

`anticlustering`

`balanced_clustering`

`matching`

`categorical_sampling`

`wce`

[Package

*anticlust* version 0.5.6

Index]