An implementation of the gap statistic algorithm from Tibshirani, Walther, and Hastie's "Estimating the number of clusters in a data set via the gap statistic". A description of the algorithm can be found here.
# Single cluster in 5 dimensions
data = cbind(rnorm(20), rnorm(20), rnorm(20), rnorm(20), rnorm(20))
png("examples/1_cluster_5d_gaps.png")
gap_statistic(data)
dev.off()
# Three clusters in 2 dimensions
x = c(rnorm(20, mean = 0), rnorm(20, mean = 3), rnorm(20, mean = 5))
y = c(rnorm(20, mean = 0), rnorm(20, mean = 5), rnorm(20, mean = 0))
data = cbind(x, y)
png("examples/3_clusters_2d.png")
qplot(x, y)
dev.off()
png("examples/3_clusters_2d_gaps.png")
gap_statistic(data)
dev.off()
# Four clusters in 3 dimensions
x = c(rnorm(20, mean = 0), rnorm(20, mean = 3), rnorm(20, mean = 5), rnorm(20, mean = -10))
y = rnorm(80, mean = 0)
z = c(rnorm(40, mean = -5), rnorm(40, mean = 0))
data = cbind(x, y, z)
png("examples/4_clusters_3d.png")
scatterplot3d(x, y, z)
dev.off()
png("examples/4_clusters_3d_gaps.png")
gap_statistic(data)
dev.off()
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