Find Optimal Number of Clusters

Determines the optimal number of clusters to cut a hierarchical clustering tree, based on the selected information criterion (e.g., BIC or AIC).

Usage

number.of.clusters(tree, n, method = c("BIC", "AIC"))

Arguments

tree

Clustering tree of class hclust.

n

Number of samples in the original data.

method

Information criterion method. One of "BIC" or "AIC".

Value

An integer representing the optimal number of clusters.

Examples

# Example: Determine number of clusters in dummy data set
set.seed(1234)
K0 <- matrix(
  rep(c(sample(0:20, 200, replace = TRUE), sample(20:40, 200, replace = TRUE)), 2),
  nrow = 100, byrow = TRUE
)
K1 <- matrix(
  rep(c(sample(20:40, 200, replace = TRUE), sample(0:20, 200, replace = TRUE)), 2),
  nrow = 100, byrow = TRUE
)
tree <- fuse.cluster(K0, K1)
k <- number.of.clusters(tree, ncol(K0), 'BIC')
k
#> [1] 4