Clest

From RSKC v2.4.2 by Yumi Kondo

An implementation of Clest with robust sparse K-means. CER is used as a similarity measure.

The function Clest performs Clest ( Dudoit and Fridlyand (2002)) with CER as the measure of the agreement between two partitions (in each training set). The following clustering algorithm can be used: K-means, trimmed K-means, sparse K-means and robust sparse K-means.

Usage

Clest(d, maxK, alpha, B = 15, B0 = 5, nstart = 1000, 
      L1 = 6, beta = 0.1, pca = TRUE, silent=FALSE)

Arguments

d: A numerical data matrix (N by p) where N is the number of cases and p is the number of features. The cases are clustered.
maxK: The maximum number of clusters that you suspect.
alpha: See RSKC.
B: The number of times that an observed dataset d is randomly partitioned into a learning set and a training set. Note that each generated reference dataset is partitioned into a learning and a testing set only once to ease the computational cost.
B0: The number of times that the reference dataset is generated.
nstart: The number of random initial sets of cluster centers at Step(a) of robust sparse K-means clustering.
L1: See RSKC.
beta: 0 <= beta
pca: Logical, if TRUE, then reference datasets are generated from a PCA reference distribution. If FALSE, then the reference data set is generated from a simple reference distribution.
silent: Logical, if TRUE, then the number of iteration on progress is not printed.

Value

References

Yumi Kondo (2011), Robustificaiton of the sparse K-means clustering algorithm, MSc. Thesis, University of British Columbia http://hdl.handle.net/2429/37093

S. Dudoit and J. Fridlyand. A prediction-based resampling method for estimating the number of clusters in a dataset. Genome Biology, 3(7), 2002.

Examples

## Not run: 
# # little simulation function 
# sim <-
# function(mu,f){
#    D<-matrix(rnorm(60*f),60,f)
#    D[1:20,1:50]<-D[1:20,1:50]+mu
#    D[21:40,1:50]<-D[21:40,1:50]-mu  
#    return(D)
#    }
#  
#  set.seed(1)
#  d<-sim(1.5,100); # non contaminated dataset with noise variables
#  
# # Clest with robust sparse K-means
# rsk<-Clest(d,5,alpha=1/20,B=3,B0=10, beta = 0.05, nstart=100,pca=TRUE,L1=3,silent=TRUE);
# # Clest with K-means
# k<-Clest(d,5,alpha=0,B=3,B0=10, beta = 0.05, nstart=100,pca=TRUE,L1=NULL,silent=TRUE);
# ## End(Not run)

Documentation reproduced from package RSKC, version 2.4.2, License: GPL (>= 2)

Community examples

Looks like there are no examples yet.