stablecluster: Stable Sparse K-means

In sskm: Stable Sparse K-Means

Description

Perform Stable Sparse K-means algorithm to achieve sparsity and cluster observations.

Usage

stablecluster(data, k, wb, nstart = 20, maxiter = 6, orig, N, pi)

Arguments

data	List of subsamples. Each matrix should be a subsample from the same source of original data.
k	The number of clusters assumed to be in the data.
wb	A list of tuning parameters for L1 bound on weights for which to run the sparse k-means algorithm. There should be one wb value in each item of the list corresponding to each subsample. It is recommended to use the gap statistic, calculated via the function tun_calc to determine the value for wb. Note if there is a convergence issue, a new subsample will automatically be taken from original data and wb value for that subsample will automatically be chosen based on minimum gap statistic within one standard deviation of the maximum.
nstart	The number of random starts to be used in the sparse k-means and k-means algorithm. Default is 20.
maxiter	The maximum number of iterations to be used in clustering algorithms. Default is set to 6.
orig	The original dataset from which the subsamples were taken from.
N	The number of observations in original sample.
pi	Tuning parameter for the minimun proportion of times each feature needs to receive a positive weight from sparse k-means algorithm to be selected as a stable feature.

Details

To be applied on subsamples of original data. It is recommended to be applied a second time on subsamples of output dataset to perform second iteration. One can determine which tuning parameter combination to use based on prediction strength.

Value

A list containing:

stable.data	An NxF1 matrix containing the new "stable" dataset with only the stable features where N is the number of observations in original data and F1 is the number of stable features.
stable.km	The output from running standard kmeans on stable data. See kmeans documentation for more details.
table	A dataframe containing information on the number of features and prediction strength for each value of pi. pi.j is the value of pi. F1 is the number of features selected as stable. pred.str is the prediction strength. See fpc documentation on prediction strength for more details. https://cran.r-project.org/package=fpc

Author(s)

Abraham Apfel

References

Witten and Tibshirani (2009) A framework for feature selection in clustering.

Tibshirani, R. and Walther, G. (2005) Cluster Validation by Prediction Strength, Journal of Computational and Graphical Statistics, 14, 511-528.

Examples

#Simulate data matrix
dat1<-matrix(rnorm(200,-1,1),20,10)
dat2<-matrix(rnorm(800,0,1),20,40)
C1<-cbind(dat1,dat2)
C2<-matrix(rnorm(1000,0,1),20,50)
dat3<-matrix(rnorm(200,1,1),20,10)
dat4<-matrix(rnorm(800,0,1),20,40)
C3<-cbind(dat3,dat4)
orig.sample<-rbind(C1,C2,C3)

#Take B=4 subsamples
sub.sample<-sub.sim(data=orig.sample,N=60,prop=0.5,B=4)

#Calculate gap statistic to aid in tuning parameter selection for each subsample
tun_par<-tun_calc(data=sub.sample,k=3,wb=NULL,nperms=5,quiet=FALSE)

#Create list based on highest gap statistic to be used as wb parameter
max.gap<-replicate(n=4,expr=list())
for(i in 1:4){
  max.gap[[i]]<-tun_par[[i]]$bestw
}
pi1<-c(0.2,0.3,0.4)
#Apply Stable Sparse K-means algorithm on subsamples
res<-stablecluster(data=sub.sample,k=3,wb=max.gap,nstart=5,maxiter=6,orig=orig.sample,N=60,pi=pi1)

sskm documentation built on May 29, 2017, 10:43 p.m.