Estimate Optimal Number of Cluster for PAM Algorithm

cluster::clusGap() cannot be used here for distance matrix, so it is removed.

cluster_pam_estimate(
  x,
  method = c("silhouette", "wss"),
  k.max = 10,
  verbose = interactive(),
  barfill = "steelblue",
  barcolor = "steelblue",
  linecolor = "steelblue",
  FUNcluster = cluster::pam,
  seed = 1234L,
  ...
)

cluster_pam(x, k, ...)

Arguments

x	a dissimilarity matrix.
method	the method to be used for estimating the optimal number of clusters. Possible values are "silhouette" (for average silhouette width), "wss" (for total within sum of square) and "gap_stat" (for gap statistics).
k.max	the maximum number of clusters to consider, must be at least two.
verbose	logical value. If TRUE, the result of progress is printed.
barfill	fill color and outline color for bars
barcolor	fill color and outline color for bars
linecolor	color for lines
FUNcluster	a partitioning function which accepts as first argument a (data) matrix like x, second argument, say k, k >= 2, the number of clusters desired, and returns a list with a component named cluster which contains the grouping of observations. Allowed values include: kmeans, cluster::pam, cluster::clara, cluster::fanny, hcut, etc. This argument is not required when x is an output of the function NbClust::NbClust().
seed	random seed.
...	other parameters passing to cluster::pam.
k	positive integer specifying the number of clusters, less than the number of observations.

Value

a ggplot object.

a PAM clustering result object.

Examples

data("iris")
head(iris)
iris.scaled <- scale(iris[, -5])
iris.dist <- dist(iris.scaled) %>% as.matrix()
p <- cluster_pam_estimate(iris.dist)
p2 <- cluster_pam_estimate(iris.dist, method = "wss")

cl <- cluster_pam(iris.dist, 3)