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

## Description

K-means++ algorithm is usually used as a fast initialization scheme, though it can still be used as a standalone clustering algorithms by first choosing the centroids and assign points to the nearest centroids.

## Usage

 1 kmeanspp(data, k = 2) 

## Arguments

 data an (n \times p) matrix of row-stacked observations. k the number of clusters (default: 2).

## Value

a named list of S3 class T4cluster containing

cluster

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

algorithm

name of the algorithm.

## References

\insertRef

arthur_k-means++:_2007T4cluster

## Examples

  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)$Y ## CLUSTERING WITH DIFFERENT K VALUES cl2 = kmeanspp(X, k=2)$cluster cl3 = kmeanspp(X, k=3)$cluster cl4 = kmeanspp(X, k=4)$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) 

T4cluster documentation built on Aug. 16, 2021, 9:07 a.m.