tkmeans {lowmemtkmeans}

Trimmed k-means clustering

Description

Performs trimmed k-means clustering algorithm [1] on a matrix of data. Each row in the data is an observation, each column is a variable. For optimal use columns should be scaled to have the same means and variances using scale_mat_inplace.

Usage

tkmeans(M, k, alpha, weights = rep(1, ncol(M)), nstart = 1L, iter = 10L,
  tol = 1e-04, verbose = FALSE)

Arguments

M

matrix (n x m). Rows are observations, columns are predictors.

k

number of clusters

alpha

proportion of data to be trimmed

weights

weightings for variables (columns).

nstart

number of restarts

iter

maximum number of iterations

tol

criteria for algorithm convergence

verbose

If true will output more information on algorithm progress.

Details

k is the number of clusters. alpha is the proportion of data that will be excluded in the clustering.

Algorithm will halt if either maximum number of iterations is reached or the change between iterations drops below tol.

When n_starts is greater than 1, the algorithm will run multiple times and the result with the best BIC will be returned. The centres are intialised by picking k observations.

The function only returns the k cluster centres. To calculate the nearest cluster centre for each observation use the function nearest_cluster.

Value

Returns a matrix of cluster means (k x m).

References

[1] Garcia-Escudero, Luis A.; Gordaliza, Alfonso; Matran, Carlos; Mayo-Iscar, Agustin. A general trimming approach to robust cluster Analysis. Ann. Statist. 36 (2008), no. 3, 1324–1345.

Examples

iris_mat <- as.matrix(iris[,1:4])
scale_params<-scale_mat_inplace(iris_mat)
iris_cluster<- tkmeans(iris_mat, 2 , 0.1, c(1,1,1,1), 1, 10, 0.001) # 2 clusters