Test out the most recent version (v0.5.4) of anticlust

test_anticlust.R

## 1. Load - and, if required, install - package `anticlust`

if (!requireNamespace("remotes")) {
  install.packages("remotes")
}
remotes::install_github("m-Py/anticlust")

library(anticlust)


# Try out anticlustering - if there are no errors, everything is fine

# Optimize the cluster editing (diversity) criterion
anticlusters <- anticlustering(
  schaper2019[, 3:6],
  K = 3,
  categories = schaper2019$room
)
anticlusters


# Use multiple starts of the algorithm to improve the objective and
# optimize the k-means criterion ("variance")
anticlusters <- anticlustering(
  schaper2019[, 3:6],
  objective = "variance",
  K = 3,
  categories = schaper2019$room,
  method = "local-maximum",
  repetitions = 2
)
anticlusters

# Optimize dispersion
anticlusters <- anticlustering(
  dist(rnorm(100)),
  objective = "dispersion",
  K = 10
)
anticlusters