Same Size Clustering

This is a wrapper for several implementation that classify samples into same size clusters, the details please see this blog. The source code is modified based on code from the blog.

same_size_clustering(
  mat,
  diss = FALSE,
  clsize = NULL,
  algo = c("nnit", "hcbottom", "kmvar"),
  method = c("maxd", "random", "mind", "elki", "ward.D", "average", "complete", "single")
)

Arguments

mat

a data/distance matrix.

diss

if TRUE, treat mat as a distance matrix.

clsize

integer, number of sample within a cluster.

algo

algorithm.

method

method.

Value

a vector.

Examples

set.seed(1234L)
x <- rbind(
  matrix(rnorm(100, sd = 0.3), ncol = 2),
  matrix(rnorm(100, mean = 1, sd = 0.3), ncol = 2)
)
colnames(x) <- c("x", "y")

y1 <- same_size_clustering(x, clsize = 10)
y11 <- same_size_clustering(as.matrix(dist(x)), clsize = 10, diss = TRUE)

y2 <- same_size_clustering(x, clsize = 10, algo = "hcbottom", method = "ward.D")

y3 <- same_size_clustering(x, clsize = 10, algo = "kmvar")
y33 <- same_size_clustering(as.matrix(dist(x)), clsize = 10, algo = "kmvar", diss = TRUE)
#> PAM algorithm is applied when input distance matrix.