R/clustering.R

   1 #main function (choice between kmeans and hierarchical clustering)
   2 getClusters = function(distances, method, K)
   3 {
   4     clusts = c()
   5     if (method=="KM")
   6     {
   7         nstart = 10 #number of kmeans random restarts
   8         maxiter = 100 #maximum iterations count in each km run
   9         clusts = .Call("kmeansWithDistances", distances, K, nstart, maxiter)
  10     }
  11     else if (method=="HC")
  12     {
  13         #simple hierarchical clustering using ECT distances
  14         hct = hclust(as.dist(distances),method="ward.D")
  15         clusts = cutree(hct, K)
  16     }
  17     return (clusts)
  18 }
  19
  20 # renumbering step (post-processing after clustering)
  21 reordering = function(clusts)
  22 {
  23     newCl = clusts
  24     maxInd = max(clusts)
  25     counter = 1
  26     for (i in 1:maxInd)
  27     {
  28         if (sum(clusts == i) > 0)
  29         {
  30             newCl[clusts == i] = counter
  31             counter = counter + 1
  32         }
  33     }
  34     return (newCl)
  35 }