# kmeans: K-Means Clustering In T4cluster: Tools for Cluster Analysis

## Description

K-means algorithm we provide is a wrapper to the Armadillo's k-means routine. Two types of initialization schemes are employed. Please see the parameters section for more details.

## Usage

 1 kmeans(data, k = 2, ...) 

## Arguments

 data an (n\times p) matrix of row-stacked observations. k the number of clusters (default: 2). ... extra parameters including initinitialization method; either "random" for random initialization, or "plus" for k-means++ starting. maxiterthe maximum number of iterations (default: 10). nstartthe number of random initializations (default: 5).

## Value

a named list of S3 class T4cluster containing

cluster

a length-n vector of class labels (from 1:k).

mean

a (k\times p) matrix where each row is a class mean.

wcss

within-cluster sum of squares (WCSS).

algorithm

name of the algorithm.

## References

\insertRef

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 # ------------------------------------------------------------- # clustering with 'iris' dataset # ------------------------------------------------------------- ## PREPARE data(iris) X = as.matrix(iris[,1:4]) lab = as.integer(as.factor(iris[,5])) ## EMBEDDING WITH PCA X2d = Rdimtools::do.pca(X, ndim=2)$Y ## CLUSTERING WITH DIFFERENT K VALUES cl2 = kmeans(X, k=2)$cluster cl3 = kmeans(X, k=3)$cluster cl4 = kmeans(X, k=4)$cluster ## VISUALIZATION opar <- par(no.readonly=TRUE) par(mfrow=c(1,4), pty="s") plot(X2d, col=lab, pch=19, main="true label") plot(X2d, col=cl2, pch=19, main="k-means: k=2") plot(X2d, col=cl3, pch=19, main="k-means: k=3") plot(X2d, col=cl4, pch=19, main="k-means: k=4") par(opar)