mohammed0115/kmeans

R implementation of k-means clustering algorithm

K-Means Clustering Algortihm

General description

A R implementation of k-means clustering algorithm.

Input

A matrix of paired points and number of cluster

Output

The clusters of points and centers for each cluster

Usage

Image Compression:

• kmeans with different cluster number

a2 = KM(Data,2)
a4 = KM(Data,4)
a6 = KM(Data,6)

• Show compressed images

plotgrid = function(a){
  data = a[[2]]
  newmat = matrix(data$newdata,nrow = nrow(Data),ncol= ncol(Data))
  ## transfer to raster file for plot
  rasterGrob(newmat)
}

k1 = rasterGrob(Data)
k2 = plotgrid(a2)
k4 = plotgrid(a4)
k6 = plotgrid(a6)

grid.arrange(k1,k2,k4,k6, ncol=2,nrow = 2)

imagE = function(a){
  data = a[[2]]
  newmat = matrix(data$newdata,nrow = nrow(Data),ncol= ncol(Data))
  #transfer in to matrix, the original form 
  newmat = apply(newmat,2,rev)
  return(newmat)
}

i2 = imagE(a2)
i4 = imagE(a4)
i6 = imagE(a6)


image(t(Data),col = grey(seq(0,1,length = 256)),axes = F,main = "Original")
image(t(i2), col = grey(seq(0,1,length = 256)),axes = F,main = "K = 2")
image(t(i4), col = grey(seq(0,1,length = 256)),axes = F,main = "K = 4")
image(t(i6), col = grey(seq(0,1,length = 256)),axes = F,main = "K = 6")