parallel k-means in R

Question

I am trying to understand how to parallelize some of my code using R. So, in the following example I want to use k-means to cluster data using 2,3,4,5,6 centers, while using 20 iterations. Here is the code:

library(parallel)
library(BLR)

data(wheat)

parallel.function <- function(i) {
    kmeans( X[1:100,100], centers=?? , nstart=i )
}

out <- mclapply( c(5, 5, 5, 5), FUN=parallel.function )

How can we parallel simultaneously the iterations and the centers? How to track the outputs, assuming I want to keep all the outputs from k-means across all, iterations and centers, just to learn how?

user_1443620 · Answer

There's a CRAN package called knor that is derived from a research paper that improves the performance using a memory efficient variant of Elkan's pruning algorithm. It's an order of magnitude faster than everything in these answers.

install.packages("knor")
require(knor)
iris.mat <- as.matrix(iris[,1:4])
k <- length(unique(iris[, dim(iris)[2]])) # Number of unique classes
nthread <- 4
kms <- Kmeans(iris.mat, k, nthread=nthread)

parallel k-means in R

3 Answers