kmeans: k-means function

In MajKMeans: k-Means Algorithm with a Majorization-Minimization Method

Description

k-means algorithm in clustering. This function export the clustered results based on one replication of the k-means method

Arguments

x	matrix of data (dim 1: samples (must be equal to dim 1 of X), dim 2: attributes (must be equal to dim 2 of X))
centers	initial seleted centroids (randomly or another method)
distFun	function (in this package the distance is Euclidian)
nItter	Number of itteration function

Value

clustered results based on k-means methods.

Examples

{
X=rbind(matrix(rnorm(1000*2 ,4,.1),1000,2),matrix(rnorm(1000*2, 3, 0.2),1000,2))
M <- X[sample(nrow(X), 2),]
kmeans(X,M, Euclid, 4)
}

MajKMeans documentation built on March 31, 2020, 5:17 p.m.