Using BIC, AIC to estimate the number of clusters in document clustering using Kmeans

Question

Using BIC, AIC to estimate the number of clusters in document clustering using Kmeans

In my approach, I am trying to find the optimal "k" value for clustering a set of documents using the KMEANS algorithm. I wanted to use the "AIC" and "BIC" function to find the best model. I used this resource "sherrytowers.com/2013/10/24/k-means-clustering/" to find the best "k" value.

But I got the following graphs for AIC and BIC when I ran the code. I cannot interpret anything from the graphs. my doubts:

Is my approach wrong and these measures (AIC, BIC) cannot be used to cluster documents using Kmeans?
Or there are errors in the programming logic, and "AIC" and "BIC" is the correct way to find the "k" number of clusters?

Here's my code

library(tm)
library(SnowballC)
corp <- Corpus(DirSource("/home/dataset/"), readerControl = list(blank.lines.skip=TRUE));  ## forming Corpus from document set 
corp <- tm_map(corp, stemDocument, language="english")
dtm <- DocumentTermMatrix(corp,control=list(minwordlength = 1)) ## forming Document Term Matrix
dtm_tfidf <- weightTfIdf(dtm)
m <- as.matrix(dtm_tfidf)
norm_eucl <- function(m) m/apply(m, MARGIN=1, FUN=function(x) sum(x^2)^.5)
m_norm <- norm_eucl(m)

kmax = 50

totwss = rep(0,kmax) # will be filled with total sum of within group sum squares
kmfit = list() # create and empty list
for (i in 1:kmax){
  kclus = kmeans(m_norm,centers=i,iter.max=20)
  totwss[i] = kclus$tot.withinss
  kmfit[[i]] = kclus
}

kmeansAIC = function(fit){

  m = ncol(fit$centers)
  n = length(fit$cluster)
  k = nrow(fit$centers)
  D = fit$tot.withinss
  return(D + 2*m*k)
}
aic=sapply(kmfit,kmeansAIC)
plot(seq(1,kmax),aic,xlab="Number of clusters",ylab="AIC",pch=20,cex=2)


kmeansBIC = function(fit){

  m = ncol(fit$centers)
  n = length(fit$cluster)
  k = nrow(fit$centers)
  D = fit$tot.withinss
  return(D + log(n)*m*k)
}
bic=sapply(kmfit,kmeansBIC)
plot(seq(1,kmax),bic,xlab="Number of clusters",ylab="BIC",pch=20,cex=2)