Skip to contents

Batch K-Means Clustering

Source: R/batch.R
kmeans_batch.Rd

Clusters a numeric matix into k groups.

Usage

kmeans_batch(X, k, max_iter = 100L, tol = 1e-06)

Arguments

X

a numeric matrix where each row is an observation

k

a positive integer, number of clusters

max_iter

a positive integer, maximum number of iterations (default = 100)

tol

a positive numeric, convergance tolerance on centroids shift (default = 1e-6)

Value

a list with two components:

  • 'centers': a matrix of clustering centroids (dim: k x ncol(X))

  • 'assignments': an integer vector of clustering labels (1-indexed)

  • 'iterations': an integer of the number of completed iterations

Examples

# simple 2D example with three well-separated clusters
set.seed(42)
cluster1 <- matrix(rnorm(40, mean = 0,  sd = 0.5), nrow = 20)
cluster2 <- matrix(rnorm(40, mean = 5,  sd = 0.5), nrow = 20)
cluster3 <- matrix(rnorm(40, mean = 10, sd = 0.5), nrow = 20)
X <- rbind(cluster1, cluster2, cluster3)
X <- X[sample(nrow(X)),] # for better initial centers
result <- kmeans_batch(X, k = 3L, max_iter = 100L, tol = 1e-6)
#> iter 1: shift = 0.6887
#>   center 1: (0.0960, -0.1355)
#>   center 2: (10.0410, 10.0077)
#>   center 3: (4.9995, 5.0803)
#> iter 2: shift = 0.0000
#>   center 1: (0.0960, -0.1355)
#>   center 2: (10.0410, 10.0077)
#>   center 3: (4.9995, 5.0803)
result$centers
#>             [,1]       [,2]
#> [1,]  0.09596001 -0.1354959
#> [2,] 10.04097793 10.0076688
#> [3,]  4.99954464  5.0803004
result$assignments
#>  [1] 1 2 3 3 2 3 1 1 2 1 2 1 3 1 2 3 3 1 2 1 1 1 3 2 1 2 2 2 1 1 3 1 3 3 1 1 2 3
#> [39] 3 1 2 2 2 3 3 3 2 2 1 3 2 1 1 3 3 2 2 2 3 3
result$iterations
#> [1] 2

# plot the result
plot(X[, 1], X[, 2],
     col = result$assignments,
     pch = 16, cex = 1.5,
     xlab = "X1", ylab = "X2",
     main = "Batch K-Means")
points(result$centers[, 1], result$centers[, 2],
       col = 1:3, pch = 4, cex = 3, lwd = 3)