The tkmeans package attempts to implement the trimmed k-means algorithm of GarcĂ­a-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])
  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

    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: