trimcluster (version 0.2-0)

trimkmeans: Trimmed k-means clustering

Description

The trimmed k-means clustering method by Cuesta-Albertos, Gordaliza and Matran (1997). This optimizes the k-means criterion under trimming a portion of the points.

Usage

trimkmeans(data,k,trim=0.1, scaling=FALSE, 
        runs=500, niter1=3, niter2=20, nkeep=5, points=NULL,
        countmode, printcrit, maxit,
        parallel=FALSE, n.cores=-1, trace=0, ...)
  # S3 method for tkm
print(x, ...)
  # S3 method for tkm
plot(x, data, ...)

Value

An object of class 'tkm' which is a LIST with components

classification

integer vector coding cluster membership with trimmed observations coded as k+1.

means

numerical matrix giving the mean vectors of the k classes.

disttom

vector of squared Euclidean distances of all points to the closest mean.

ropt

maximum value of disttom so that the corresponding point is not trimmed.

k

see above.

trim

see above.

runs

see above.

scaling

see above.

Arguments

data

matrix or data.frame with raw data

k

integer. Number of clusters.

trim

numeric between 0 and 1. Proportion of points to be trimmed.

scaling

logical. If TRUE, the variables are centered at their means and scaled to unit variance before execution.

runs

The number of random initializations to be performed.

niter1

The number of concentration steps to be performed for the nstart initializations.

niter2

The maximum number of concentration steps to be performed for the nkeep solutions kept for further iteration. The concentration steps are stopped, whenever two consecutive steps lead to the same data partition.

nkeep

The number of iterated initializations (after niter1 concentration steps) with the best values in the target function that are kept for further iterations

points

NULL or a matrix with k vectors used as means to initialize the algorithm. If initial mean vectors are specified, runs should be 1 (otherwise the same initial means are used for all runs).

countmode

(deprecated) optional positive integer. Every countmode algorithm runs trimkmeans shows a message.

printcrit

(deprecated) logical. If TRUE, all criterion values (mean squares) of the algorithm runs are printed.

maxit

(deprecated, use the combination nkeep, niter1 and niter2) The maximum number of concentration steps to be performed. The concentration steps are stopped, whenever two consecutive steps lead to the same data partition.

parallel

A logical value, specifying whether the nstart initializations should be done in parallel.

n.cores

The number of cores to use when paralellizing, only taken into account if parallel=TRUE.

trace

Defines the tracing level, which is set to 0 by default. Tracing level 1 gives additional information on the stage of the iterative process.

x

object of class tkm.

...

further arguments to be transferred to plot or plotcluster.

Author

Christian Hennig http://www.homepages.ucl.ac.uk/~ucakche/

Details

The function trimkmeans() now calls the function tkmeans() from the package tclust. This makes the procedure much faster since (a) tkmeans() is implemented in C++, (b) a new random initialization is introduced (see the parameters niter1, niter2 and nkeep which replace the previous maxit and (c) it is posible to run the initialization in parallel (see the argument parallel and ncores.

plot.tkm calls plotcluster if the dimensionality of the data p is 1, shows a scatterplot with non-trimmed regions if p=2 and discriminant coordinates computed from the clusters (ignoring the trimmed points) if p>2.

References

Cuesta-Albertos, J. A., Gordaliza, A., and Matran, C. (1997) Trimmed k-Means: An Attempt to Robustify Quantizers, Annals of Statistics, 25, 553-576.

See Also

plotcluster

Examples

  set.seed(10001)
  n1 <-60
  n2 <-60
  n3 <-70
  n0 <-10
  nn <- n1+n2+n3+n0
  pp <- 2
  X <- matrix(rep(0,nn*pp),nrow=nn)
  ii <-0
  for (i in 1:n1){
    ii <-ii+1
    X[ii,] <- c(5,-5)+rnorm(2)
  }
  for (i in 1:n2){
    ii <- ii+1
    X[ii,] <- c(5,5)+rnorm(2)*0.75
  }
  for (i in 1:n3){
    ii <- ii+1
    X[ii,] <- c(-5,-5)+rnorm(2)*0.75
  }
  for (i in 1:n0){
    ii <- ii+1
    X[ii,] <- rnorm(2)*8
  }
  tkm1 <- trimkmeans(X, k=3, trim=0.1, runs=5)
## runs=5 is used to save computing time; runs must be >= nkeep

  print(tkm1)
  plot(tkm1,X)