clusters_MajKm: clustering results of the majorized k-mean algorithm

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

clustering results of the majorized k-mean algorithm

Description

clusters data into two clusters with a majorization k-means This functionis use a hybrid of the k-means and the majorizaion-minimazation method to cluster the data and exports the clustering results as well as the sum of square (SS) of clustering

Usage

clusters_MajKm(X, k = 2, La)

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))
k	number of clusters ( this version considers 2 clusters )
La	the tunnung parameter

Value

sum of square (SS) of clustring and the 'delta' (difference of two successive majorization function).

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),]
clusters_MajKm(X,2, 0.5)
}

MajMinKmeans documentation built on May 29, 2024, 8:30 a.m.