MajMinOptim {MajMinKmeans}

majorization-minimization optimization

Description

Finding the optimized majorization-minimization centers

Usage

MajMinOptim(X, Z, M, eps, lambda)

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))

Z

is a n by k matrix where for all i and j, zi,j is abinary variable that is equal to 1 if the case i is assigned to cluster j and zero otherwise. (dim 1: samples (must be equal to dim 1 of X), dim 2: attributes (must be equal to dim 2 of X))

M

initial seleted centroids (randomly or another method)

eps

a threshold value assumed as 0.0001

lambda

a threshold value assumed as 0.5

Value

The optimized majorization-minimization centers.

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),]
distsToCenters <- Euclid(X, M)
clusters <- apply(distsToCenters, 1, which.min)
Z <- matrix(0, nrow = NROW(X), ncol = 1)
for(i in 1:NROW(X))
if (clusters[[i]] == 1)
    Z[i,]=clusters[[i]]
Z=cbind(Z, 1-Z)
MajMinOptim(X,Z,M ,eps=1e-4, lambda=.5)
}