Description
Low Memory Use Trimmed K-Means.
Description
Performs the trimmed k-means clustering algorithm with lower memory use. It also provides a number of utility functions such as BIC calculations.
README.md
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:
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)
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)Calculate BIC
BIC_2 <-cluster_BIC(iris_mat, iris_cluster_2) BIC_3 <-cluster_BIC(iris_mat, iris_cluster_3)Allocate using 3 clustering
clustering <- nearest_cluster(iris_mat, iris_cluster_3)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")