Anticlustering partitions a pool of elements into clusters (or anticlusters) with the goal of achieving high between-cluster similarity and high within-cluster heterogeneity. This 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 package anticlust implements anticlustering algorithms as described in Papenberg and Klau (2021; https://doi.org/10.1037/met0000301). It was originally developed to stimuli items to experimental conditions in experimental psychology, but it can be applied whenever a user requires that a given set of elements has to be partitioned groups that have to be as similar as possible, or when the within-group heterogeneity should be high.

## Installation

The stable release of anticlust is available from CRAN and can be installed via:

install.packages("anticlust")

A (potentially more recent) version of anticlust can also be installed directly via Github:

library("remotes") # if not available: install.packages("remotes")
install_github("m-Py/anticlust")

## Citation

If you use anticlust in your research, it would be courteous if you cite the following reference:

Papenberg, M., & Klau, G. W. (2021). Using anticlustering to partition data sets into equivalent parts. Psychological Methods, 26(2), 161–174. https://doi.org/10.1037/met0000301

## How do I learn about anticlust

This README contains some basic information on the R package anticlust. More information is available via the following sources:

• A paper is available describing the theoretical background of anticlustering and the anticlust package in detail (https://doi.org/10.1037/met0000301). The freely available preprint can be retrieved from https://psyarxiv.com/3razc/.

• The package website contains all relevant documentation. This includes a vignette detailing how to use the anticlust package for stimulus selection in experiments and documentation for the main anticlust functions anticlustering(), balanced_clustering() and matching().

## A quick start

In this initial example, I use the main function anticlustering() to create three similar sets of plants using the classical iris data set:

# load the package via
library("anticlust")

anticlusters <- anticlustering(
iris[, -5],
K = 3,
objective = "variance",
method = "exchange"
)

# The output is a vector that assigns a group (i.e, a number
# between 1 and K) to each input element:
anticlusters
#>   [1] 1 3 1 1 3 2 2 3 3 3 1 2 3 1 1 2 1 2 2 1 3 2 3 2 3 3 1 3 1 2 1 2 3 2 2 1 3
#>  [38] 3 3 3 2 3 1 2 2 2 1 1 1 1 3 1 2 1 1 3 2 2 1 1 1 2 1 3 2 3 1 2 2 2 3 2 3 3
#>  [75] 1 1 2 2 1 3 1 2 3 1 1 1 3 2 3 2 3 3 2 1 3 2 2 3 2 3 3 3 3 2 2 1 2 1 1 1 1
#> [112] 3 3 1 3 1 2 1 2 1 1 3 2 2 2 3 1 2 1 3 1 3 3 2 2 2 2 1 3 3 1 3 1 1 3 3 3 2
#> [149] 2 2

# Each group has the same number of items:
table(anticlusters)
#> anticlusters
#>  1  2  3
#> 50 50 50

# Compare the feature means by anticluster:
by(iris[, -5], anticlusters, function(x) round(colMeans(x), 2))
#> anticlusters: 1
#> Sepal.Length  Sepal.Width Petal.Length  Petal.Width
#>         5.84         3.06         3.76         1.20
#> ------------------------------------------------------------
#> anticlusters: 2
#> Sepal.Length  Sepal.Width Petal.Length  Petal.Width
#>         5.84         3.06         3.76         1.20
#> ------------------------------------------------------------
#> anticlusters: 3
#> Sepal.Length  Sepal.Width Petal.Length  Petal.Width
#>         5.85         3.06         3.76         1.20

As illustrated in the example, we can use the function anticlustering() to create similar sets of elements. In this case “similar” primarily means that the mean values of the variables are pretty much the same across three groups. The function anticlustering() takes as input a data table describing the elements that should be assigned to sets. In the data table, each row represents an element, for example a person, word or a photo. Each column is a numeric variable describing one of the elements’ features. The table may be an R matrix or data.frame; a single feature can also be passed as a vector. The number of groups is specified through the argument K. (Alternatively, it is also possible to pass a dissimilarity matrix describing the pairwise distance between elements.)

To quantify cluster similarity, anticlust employs one of several measures that have been developed in the context of cluster analysis:

• the cluster editing “diversity” objective, setting objective = "diversity" (default)
• the k-means “variance” objective, setting objective = "variance"
• the “k-plus” objective, an extension of the k-means variance criterion, setting objective = "kplus"
• the “dispersion” objective (the minimum distance between any two elements within the same cluster), setting objective = "dispersion"

The anticlustering objectives are described in detail in the documentation (?diversity_objective, ?variance_objective, ?kplus_objective, ?dispersion_objective) and the references therein. It is also possible to optimize user-defined measures of cluster similarity, which is also described in the documentation (?anticlustering).

## Categorical constraints

Sometimes, it is required that sets are not only similar with regard to some numeric variables, but we also want to ensure that each set contains an equal number of elements of a certain category. Coming back to the initial iris data set, we may want to require that each set has a balanced number of plants of the three iris species. To this end, we can use the argument categories as follows:

anticlusters <- anticlustering(
iris[, -5],
K = 3,
categories = iris$Species ) ## The species are as balanced as possible across anticlusters: table(anticlusters, iris$Species)
#>
#> anticlusters setosa versicolor virginica
#>            1     17         17        16
#>            2     17         16        17
#>            3     16         17        17

## Matching and clustering

Anticlustering in a sense creates sets of dissimilar elements; the heterogenity within anticlusters is maximized (either using the cluster editing or k-means objective as measure of heterogenity). The anticlust package also provides functions for “classical” clustering applications: balanced_clustering() creates sets of elements that are similar while ensuring that clusters are of equal size. This is an example:

# Generate random data, cluster the data set and visualize results
N <- 1400
lds <- data.frame(var1 = rnorm(N), var2 = rnorm(N))
cl <- balanced_clustering(lds, K = 7)
plot_clusters(lds, clusters = cl, show_axes = TRUE)

The function matching() is very similar, but is usually used to find small groups of similar elements, e.g., triplets as in this example:

# Generate random data and find triplets of similar elements:
N <- 120
lds <- data.frame(var1 = rnorm(N), var2 = rnorm(N))
triplets <- matching(lds, p = 3)
plot_clusters(
lds,
clusters = triplets,
within_connection = TRUE,
show_axes = TRUE
)

## Questions and suggestions

If you have any question on the anticlust package or any suggestions (which are greatly appreciated), I encourage you to contact me via email () or Twitter, or to open an issue on the Github repository.