esmbl.stability: Estimate the stability of a clustering based on...

In bootcluster: Bootstrapping Estimates of Clustering Stability

Estimate the stability of a clustering based on non-parametric bootstrap out-of-bag scheme, with option for subsampling scheme

Description

Estimate the stability of a clustering based on non-parametric bootstrap out-of-bag scheme, with option for subsampling scheme

Usage

esmbl.stability(
  x,
  k,
  scheme = "kmeans",
  B = 100,
  hc.method = "ward.D",
  cut_ratio = 0.5,
  dist_method = "euclidean"
)

Arguments

x	data.frame of the data set where rows are observations and columns are features
k	number of clusters for which to estimate the stability
scheme	clustering method to use ("kmeans", "hc", or "spectral")
B	number of bootstrap re-samples
hc.method	hierarchical clustering method (default: "ward.D")
cut_ratio	ratio for subsampling (default: 0.5)
dist_method	distance method for spectral clustering (default: "euclidean")

Details

This function estimates the stability through out-of-bag observations It estimate the stability at the (1) observation level, (2) cluster level, and (3) overall.

Value

membership

vector of membership for each observation from the reference clustering

obs_wise

vector of estimated observation-wise stability

clust_wise

vector of estimated cluster-wise stability

overall

numeric estimated overall stability

Smin

numeric estimated Smin through out-of-bag scheme

Author(s)

Tianmou Liu

Examples

set.seed(123)
data(iris)
df <- iris[,1:4]
result <- esmbl.stability(df, k=3, scheme="kmeans")

bootcluster documentation built on April 3, 2025, 7:45 p.m.