Clest
From RSKC v2.4.2
by Yumi Kondo
An implementation of Clest with robust sparse Kmeans. 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: Kmeans, trimmed Kmeans, sparse Kmeans and robust sparse Kmeans.
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
byp
) whereN
is the number of cases andp
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 Kmeans clustering.
 L1

See
RSKC
.  beta

0 <=
beta
 pca

Logical, if
TRUE
, then reference datasets are generated from a PCA reference distribution. IfFALSE
, 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 Kmeans clustering algorithm, MSc. Thesis, University of British Columbia http://hdl.handle.net/2429/37093
S. Dudoit and J. Fridlyand. A predictionbased 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 Kmeans
# rsk<Clest(d,5,alpha=1/20,B=3,B0=10, beta = 0.05, nstart=100,pca=TRUE,L1=3,silent=TRUE);
# # Clest with Kmeans
# k<Clest(d,5,alpha=0,B=3,B0=10, beta = 0.05, nstart=100,pca=TRUE,L1=NULL,silent=TRUE);
# ## End(Not run)
Community examples
Looks like there are no examples yet.