Visualize a cluster analysis

plot_clusters(
  features,
  clusters,
  within_connection = FALSE,
  between_connection = FALSE,
  illustrate_variance = FALSE,
  show_axes = FALSE,
  xlab = NULL,
  ylab = NULL,
  xlim = NULL,
  ylim = NULL,
  main = "",
  cex = 1.2,
  cex.axis = 1.2,
  cex.lab = 1.2,
  lwd = 1.5,
  lty = 2,
  frame.plot = FALSE,
  cex_centroid = 2
)

Arguments

features: A data.frame or matrix representing the features that are plotted. Must have two columns.
clusters: A vector representing the clustering
within_connection: Boolean. Connect the elements within each clusters through lines? Useful to illustrate a graph structure.
between_connection: Boolean. Connect the elements between each clusters through lines? Useful to illustrate a graph structure. (This argument only works for two clusters).
illustrate_variance: Boolean. Illustrate the variance criterion in the plot?
show_axes: Boolean, display values on the x and y-axis? Defaults to `FALSE`.
xlab: The label for the x-axis
ylab: The label for the y-axis
xlim: The limits for the x-axis
ylim: The limits for the y-axis
main: The title of the plot
cex: The size of the plotting symbols, see par
cex.axis: The size of the values on the axes
cex.lab: The size of the labels of the axes
lwd: The width of the lines connecting elements.
lty: The line type of the lines connecting elements (see par).
frame.plot: a logical indicating whether a box should be drawn around the plot.
cex_centroid: The size of the cluster center symbol (has an effect only if illustrate_variance is TRUE)

Details

In most cases, the argument clusters is a vector returned by one of the functions anticlustering, balanced_clustering or matching. However, the plotting function can also be used to plot the results of other cluster functions such as kmeans. This function is usually just used to get a fast impression of the results of an (anti)clustering assignment, but limited in its functionality. It is useful for depicting the intra-cluster connections using argument within_connection.

Author

Martin Papenberg martin.papenberg@hhu.de

Examples


N <- 15
features <- matrix(runif(N * 2), ncol = 2)
K <- 3
clusters <- balanced_clustering(features, K = K)
anticlusters <- anticlustering(features, K = K)
user_par <- par("mfrow")
par(mfrow = c(1, 2))
plot_clusters(features, clusters, main = "Cluster editing", within_connection = TRUE)
plot_clusters(features, anticlusters, main = "Anticluster editing", within_connection = TRUE)

par(mfrow = user_par)