R/hkclusplus.R

In hkclustering: Ensemble Clustering using K Means and Hierarchical Clustering

library(stats)
library(cluster)

hkclusplus <-
function(df,t){
 
  scaled.df <- scale(df)
  
  numbk<-which.max(cluster::clusGap(scaled.df, FUN = kmeans, K.max = 8, B = 200)$Tab[,3])
  
  rm(.Random.seed, envir=globalenv())
  temp  <-stats::kmeans(scaled.df,numbk)
  c     <-temp$centers
    
  for (i in 2:t){
    rm(.Random.seed, envir=globalenv())
    temp <-stats::kmeans(scaled.df,numbk)
    c    <-rbind(c,temp$centers)
  }
    
  cr  <- as.data.frame(c,row.names = F)
  d   <- stats::dist(cr, method = "euclidean")
  fit <- stats::hclust(d, method="centroid") 
    
  cr$clusnumber  <- stats::cutree(fit, k=numbk)
  centroids1     <- stats::aggregate(cr, by=list(cr$clusnumber), FUN = mean)
    
  centr          <- centroids1[,c(2:(length(df)+1))]
  
  final          <- stats::kmeans(scaled.df,centr)
  
  clustereddata  <- cbind(df,final$cluster)
  
  colnames(clustereddata)[(length(df)+1)]  <-"cluster_number"
  
  return(clustereddata)
  
}