bicriterion_anticlustering {anticlust}R Documentation

Bicriterion iterated local search heuristic


This function implements the bicriterion for anticlustering by Brusco, Cradit, and Steinley (2020; <doi:10.1111/bmsp.12186>). The description of the algorithm is given in Section 3 of their paper (in particular, see the pseudocode in their Figure 2).


  R = NULL,
  W = c(1e-06, 1e-05, 1e-04, 0.001, 0.01, 0.1, 0.5, 0.99, 0.999, 0.999999),
  Xi = c(0.05, 0.1)



The data input. Can be one of two structures: (1) A feature matrix where rows correspond to elements and columns correspond to variables (a single numeric variable can be passed as a vector). (2) An N x N matrix dissimilarity matrix; can be an object of class dist (e.g., returned by dist or as.dist) or a matrix where the entries of the upper and lower triangular matrix represent pairwise dissimilarities.


How many anticlusters should be created. Alternatively: (a) A vector describing the size of each group, or (b) a vector of length nrow(x) describing how elements are assigned to anticlusters before the optimization starts.


The desired number of restarts for the algorithm. By default, both phases (MBPI + BILS) of the algorithm are performed once.


Optional argument, a vector of weights defining the relative importance of dispersion and diversity (0 <= W <= 1). See details.


Optional argument, specifies probability of swapping elements during the iterated local search. See examples.


The bicriterion algorithm by Brusco, Cradit, and Steinley (2020) aims to simultaneously optimize two anticlustering criteria: the diversity_objective and the dispersion_objective. It returns a list of partitions that approximate the pareto set of efficient solutions across both criteria. By considering both the diversity and dispersion, this algorithm is well-suited for maximizing overall within-group heterogeneity. To select a partition among the approximated pareto set, it is reasonable to plot the objectives for each partition (see Examples).

The arguments R, W and Xi are named for consistency with Brusco et al. (2020). The argument K is used for consistency with other functions in anticlust; Brusco et al. used 'G' to denote the number of groups. However, note that K can not only be used to denote the number of equal-sized groups, but also to specify group sizes, as in anticlustering.

This function implements the combined bicriterion algorithm MBPI + BILS. The argument R denotes the number of restarts of the search heuristic. Half of the repetitions perform MBPI and the other half perform BILS, as suggested by Brusco et al. The argument W denotes the possible weights given to the diversity criterion in a given run of the search heuristic. In each run, the a weight is randomly selected from the vector W. As default values, we use the weights that Brusco et al. used in their analyses. All values in w have to be in [0, 1]; larger values indicate that diversity is more important, whereas smaller values indicate that dispersion is more important; w = .5 implies the same weight for both criteria. The argument Xi is the probability that an element is swapped during the iterated local search (specifically, Xi has to be a vector of length 2, denoting the range of a uniform distribution from which the probability of swapping is selected). For Xi, the default is selected consistent with the analyses by Brusco et al.

If the data input x is a feature matrix (that is: each row is a "case" and each column is a "variable"), a matrix of the Euclidean distances is computed as input to the algorithm. If a different measure of dissimilarity is preferred, you may pass a self-generated dissimilarity matrix via the argument x.


A matrix of anticlustering partitions (i.e., the approximated pareto set). Each row corresponds to a partition, each column corresponds to an input element.


For technical reasons, the pareto set returned by this function has a limit of 500 partitions. Usually however, the algorithm usually finds much fewer partitions. There is one following exception: We do not recommend to use this method when the input data is one-dimensional where the algorithm may identify too many equivalent partitions causing it to run very slowly (see section 5.6 in Breuer, 2020).


Martin Breuer, Martin Papenberg


Brusco, M. J., Cradit, J. D., & Steinley, D. (2020). Combining diversity and dispersion criteria for anticlustering: A bicriterion approach. British Journal of Mathematical and Statistical Psychology, 73, 275-396.

Breuer (2020). Using anticlustering to maximize diversity and dispersion: Comparing exact and heuristic approaches. Bachelor thesis.


# Generate some random data
M <- 3
N <- 80
K <- 4
data <- matrix(rnorm(N * M), ncol = M)

# Perform bicriterion algorithm, use 200 repetitions
pareto_set <- bicriterion_anticlustering(data, K = K, R = 200)

# Compute objectives for all solutions
diversities_pareto <- apply(pareto_set, 1, diversity_objective, x = data)
dispersions_pareto <- apply(pareto_set, 1, dispersion_objective, x = data)

# Plot the pareto set
  col = "blue",
  cex = 2,
  pch = as.character(1:NROW(pareto_set))

# Get some random solutions for comparison
rnd_solutions <- t(replicate(n = 200, sample(pareto_set[1, ])))

# Compute objectives for all random solutions
diversities_rnd <- apply(rnd_solutions, 1, diversity_objective, x = data)
dispersions_rnd <- apply(rnd_solutions, 1, dispersion_objective, x = data)

# Plot random solutions and pareto set. Random solutions are far away 
# from the good solutions in pareto set
  diversities_rnd, dispersions_rnd, 
  col = "red",
  xlab = "Diversity",
  ylab = "Dispersion",
  ylim = c(
    min(dispersions_rnd, dispersions_pareto), 
    max(dispersions_rnd, dispersions_pareto)
  xlim = c(
    min(diversities_rnd, diversities_pareto), 
    max(diversities_rnd, diversities_pareto)

# Add approximated pareto set from bicriterion algorithm:
points(diversities_pareto, dispersions_pareto, col = "blue", cex = 2, pch = 19)

[Package anticlust version 0.8.5 Index]