r - How to run parallel Elbow method to find appropriate k-clusters -

the "data.clustering" data frame size: 943x2

> head(data.clustering)   age gender 2   2      1 3   6      2 4   2      1 5   2      1 6   6      2 7   6      1 

when found k values using elbow method:

elbow.k <- function(mydata){   ## determine "good" k using elbow   dist.obj <- dist(mydata);   hclust.obj <- hclust(dist.obj);   css.obj <- css.hclust(dist.obj,hclust.obj);   elbow.obj <- elbow.batch(css.obj);   #   print(elbow.obj)   k <- elbow.obj$k   return(k) }  # find k value start.time <- sys.time(); k.clusters <- elbow.k(data.clustering); end.time <- sys.time(); cat('time find k using elbow method is',(end.time - start.time),'seconds k value:', k.clusters);  time large:  time find k using elbow method 24.01472 seconds k value: 10 

can me use parallel in r reduce time of elbow method ? lot.

you can use library(parallel) package in r. must consider import variable , package enviroment using clusterevalq(), clusterexport(). think code below: library(parallel)

#

elbow.k <- function(mydata){   ## determine "good" k using elbow   dist.obj <- dist(mydata);   hclust.obj <- hclust(dist.obj);   css.obj <- css.hclust(dist.obj,hclust.obj);   elbow.obj <- elbow.batch(css.obj);   #   print(elbow.obj)   k <- elbow.obj$k   return(k) }  # find k value no_cores <- detectcores(); cl<-makecluster(no_cores); clusterevalq(cl, library(gmd)); clusterexport(cl, list("clustering.kmeans")); // add variables , functions enviroment start.time <- sys.time(); k.clusters <- parsapply(cl, 1, function(x) elbow.k(data.clustering)); // or parlapply - returns list.  end.time <- sys.time(); cat('time find k using elbow method is',(end.time - start.time),'seconds k value:', k.clusters); stopcluster(cl);