MKMeans (version 3.3)

MKMeans-package: Modern K-Means (MKMeans) Clustering.

Description

It's a Modern K-Means clustering algorithm which works for data of any number of dimensions, has no limit with the number of clusters expected, and can start with any initial cluster centers.

Author

Yarong Yang, Nader Ebrahimi, Yoram Rubin, and Jacob Zhang

Details

Package:	MKMeans
Type:	Package
Version:	3.3
Date:	2025-08-27
License:	GPL-2

References

Yarong Yang, Nader Ebrahimi, Yoram Rubin, and Jacob Zhang.(2025) MKMeans: A Modern K-Means Clustering Algorithm. technical report

Examples

# Example 1:

# Generate 20 bivarate samples
x<-rnorm(20,0,1)
y<-rnorm(20,1,1)
data.test<-cbind(x,y)

# Conduct MKMeans analysis with K=3 and taking the first 3 samples as initial cluster centers 
Res<-MKMeans(data.test,3,1,iteration=1000,type=1)
Ress<-Res
names(Ress@Classes[[1]])<-rep("red",length(Res@Classes[[1]]))
names(Ress@Classes[[2]])<-rep("blue",length(Res@Classes[[2]]))
names(Ress@Classes[[3]])<-rep("green",length(Res@Classes[[3]]))
Cols<-names(sort(c(Ress@Classes[[1]],Ress@Classes[[2]],Ress@Classes[[3]])))
plot(x,y,type="p",col=Cols,lwd=2)
points(Res@Centers,pch=15,col=c("red","blue","green"))  

# Example 2:
library(MASS)
# Generate 10 bivariate normal samples
mu1 <- c(0, 0)          
sigma1 <- matrix(c(1, 0.5, 0.5, 1), nrow=2)  
SP1 <- mvrnorm(n=10, mu=mu1, Sigma=sigma1)

# Generate another 10 bivariate normal samples
mu2<-c(1,1)
sigma2<-matrix(c(1,0,0,1),nrow=2)
SP2<-mvrnorm(n=10,mu=mu2,Sigma=sigma2)

# Generate 10 more new bivariate normal samples
mu3<-c(2,2)
sigma3<-matrix(c(1,0.5,0.5,1),nrow=2)
SP3<-mvrnorm(n=10,mu=mu3,Sigma=sigma3)

# Combine the three groups of bivariate normal samples
data<-rbind(SP1,SP2,SP3)

# Conduct MKMeans analysis with K=4 and randomly picking four samples as initial cluster centers
Res<-MKMeans(data,4,data[sample(1:30,4),],iteration=1000,type=1)
names(Res@Classes[[1]])<-rep("red",length(Res@Classes[[1]]))
names(Res@Classes[[2]])<-rep("blue",length(Res@Classes[[2]]))
names(Res@Classes[[3]])<-rep("green",length(Res@Classes[[3]]))
names(Res@Classes[[4]])<-rep("black",length(Res@Classes[[4]]))
Cols<-names(sort(c(Res@Classes[[1]],Res@Classes[[2]],Res@Classes[[3]],Res@Classes[[4]])))
plot(data[,1],data[,2],type="p",pch=19,col=Cols,lwd=2,xlab="",ylab="")
points(Res@Centers,pch=5,col=c("red","blue","green","black"))