anticlust

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 () together with the interface package 'Rglpk' (), or the SYMPHONY ILP solver () together with the interface 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.