k.select {bootcluster}  R Documentation 
Estimate number of clusters by bootstrapping stability
k.select(x, range = 2:7, B = 20, r = 5, threshold = 0.8, scheme_2 = TRUE)
x 
a 
range 
a 
B 
number of bootstrap resamplings 
r 
number of runs of kmeans 
threshold 
the threshold for determining k 
scheme_2 

This function estimates the number of clusters through a bootstrapping approach, and a measure Smin, which is based on an observationwise similarity among clusterings. The number of clusters k is selected as the largest number of clusters, for which the Smin is greater than a threshold. The threshold is often selected between 0.8 ~ 0.9. Two schemes are provided. Scheme 1 uses the clustering of the original data as the reference for stability calculations. Scheme 2 searches acrossthe clustering samples that gives the most stable clustering.
profile
a vector
of Smin measures for determining k
k
integer
estimated number of clusters
Han Yu
Bootstrapping estimates of stability for clusters, observations and model selection. Han Yu, Brian Chapman, Arianna DiFlorio, Ellen Eischen, David Gotz, Matthews Jacob and Rachael Hageman Blair.
set.seed(1) data(wine) x0 < wine[,2:14] x < scale(x0) k.select(x, range = 2:10, B=20, r=5, scheme_2 = TRUE)