# 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. ```r 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` <= 1: significance level. Clest chooses the number of clusters that returns the strongest significant evidence against the hypothesis H0 : K = 1. - `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. ## Returns - **K**: The solution of Clest; the estimated number of clusters. - **result.table**: A real matrix (`maxK-1` by 4). Each row represents `K=2`,...,`maxK` and columns represent the test statistics (=observed CER-reference CER), observed CER, reference CER and **P**-value. - **referenceCERs**: A matrix (`B0` by `maxK-1`), containing CERs of testing datasets from generated datasets for each `K=2,...,maxK`. - **observedCERs**: A matrix (`B` by `maxK-1`), containing CERs of `B` testing sets for each `K=2,...,maxK`. - **call**: The matched call. ## References Yumi Kondo (2011), Robustificaiton of the sparse K-means clustering algorithm, MSc. Thesis, University of British Columbia [http://hdl.handle.net/2429/37093](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. ## Author(s) Yumi Kondo ## Examples ```r ## 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) ```

Clest function