Clustering - identify number of clusters, analysis, and plot

clustering.r

library(NbClust)
library(factoextra)
library(Rmisc)

mydata = scale(LifeCycleSavings)
myAHCnum = NbClust(mydata, method = "ward.D", index = "all")
myNHCnum = NbClust(mydata, method = "kmeans", index = "alllong")

fig1 = fviz_nbclust(myAHCnum, method = "silhouette")
fig2 = fviz_nbclust(myNHCnum, method = "gap_stat", nboot = 100)

multiplot(fig1, fig2)

fig3 = fviz_nbclust(mydata, FUNcluster = hcut, method = "silhouette", verbose = FALSE)
fig4 = fviz_nbclust(mydata, FUNcluster = hcut, method = "wss", verbose = FALSE)
fig5 = fviz_nbclust(mydata, FUNcluster = kmeans, method = "gap_stat", nboot = 100, verbose = FALSE)

multiplot(fig3, fig4, fig5)

mykmeans = function(c, d){
  myAHC = hcut(d, k = c, stand = TRUE, graph = FALSE)
  myNHC = kmeans(scale(d), c, iter.max = 100, nstart = nrow(d))

  fig_a = fviz_dend(myAHC, rect = TRUE)
  fig_b = fviz_silhouette(myAHC, label = TRUE, rotate = TRUE, print.summary = FALSE)
  fig_c = fviz_cluster(myNHC, data = d)
  
  multiplot(fig_a)
  multiplot(fig_b)
  multiplot(fig_c)
}

for (i in c(2, 6, 15)){
  mykmeans(i, mydata)
}