Clustering: how to extract most distinguishing features?

Question

I have a set of documents that I am trying to cluster based on their vocabulary (that is, first making a corpus and then a sparse matrix with the DocumentTermMatrix

Answer 1

I had a similar problem some time ago..

Here is what I did:

require("tm")
require("skmeans")
require("slam")

# clus: a skmeans object
# dtm: a Document Term Matrix
# first: eg. 10 most frequent words per cluster
# unique: if FALSE all words of the DTM will be used
#         if TRUE only cluster specific words will be used 



# result: List with words and frequency of words 
#         If unique = TRUE, only cluster specific words will be considered.
#         Words which occur in more than one cluster will be ignored.



mfrq_words_per_cluster <- function(clus, dtm, first = 10, unique = TRUE){
  if(!any(class(clus) == "skmeans")) return("clus must be an skmeans object")

  dtm <- as.simple_triplet_matrix(dtm)
  indM <- table(names(clus$cluster), clus$cluster) == 1 # generate bool matrix

  hfun <- function(ind, dtm){ # help function, summing up words
    if(is.null(dtm[ind, ]))  dtm[ind, ] else  col_sums(dtm[ind, ])
  }
  frqM <- apply(indM, 2, hfun, dtm = dtm)

  if(unique){
    # eliminate word which occur in several clusters
    frqM <- frqM[rowSums(frqM > 0) == 1, ] 
  }
  # export to list, order and take first x elements 
  res <- lapply(1:ncol(frqM), function(i, mat, first)
                head(sort(mat[, i], decreasing = TRUE), first),
                mat = frqM, first = first)

  names(res) <- paste0("CLUSTER_", 1:ncol(frqM))
  return(res)
}

A small example:

data("crude")
dtm <- DocumentTermMatrix(crude, control =
                          list(removePunctuation = TRUE,
                               removeNumbers = TRUE,
                               stopwords = TRUE))

rownames(dtm) <- paste0("Doc_", 1:20)
clus <- skmeans(dtm, 3)


mfrq_words_per_cluster(clus, dtm)
mfrq_words_per_cluster(clus, dtm, unique = FALSE)

HTH