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The tkmeans package attempts to implement the trimmed k-means algorithm of García-Escudero, et. al.(2008) using as little memory as possible. Data is editted in place, the trimming is implemented using a priority queue structure in C++ trhough Rcpp and low memory use versions of utility functions are provided.

An extremely simple example: 1. Convert the iris dataset to a matrix and rescale matrix columns.

iris_mat <- as.matrix(iris[,1:4])
scale_params<-scale_mat_inplace(iris_mat)

1. Cluster with 2 and 3 clusters, 10% trimming

   iris_cluster_2<- tkmeans(iris_mat, 2 , 0.1, c(1,1,1,1), 1, 10, 0.001)  
   iris_cluster_3<- tkmeans(iris_mat, 2 , 0.1, c(1,1,1,1), 1, 10, 0.001)

2. Calculate BIC

   BIC_2 <-cluster_BIC(iris_mat, iris_cluster_2)  
   BIC_3 <-cluster_BIC(iris_mat, iris_cluster_3)

3. Allocate using 3 clustering

   clustering <- nearest_cluster(iris_mat, iris_cluster_3)

4. Plot results using reconstructed matrix

   library(lattice) 
   orig_matrix <- sweep(sweep(m,2,scale_params[2,],'*'),2,scale_params [1,], '+')  
   xyplot(orig_matrix[,1]~orig_matrix[,2], group=clustering) 

To install the latest version:

install.packages("devtools")  
library(devtools)  
install_github("andrewthomasjones/tkmeans")

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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