Multiple.Random.kmeans: Multiple Random k-means clustering

In clusterv: Assessment of Cluster Stability by Randomized Maps

Multiple Random k-means clustering

Description

Multiple Random k-means clusterings are computed using random projections of data. It assumes that the label of the examples are integers starting from 1 to ncol(M). Several randomized maps may be used: RS, PMO, Normal and Achlioptas random projections

Usage

Multiple.Random.kmeans(M, dim, pmethod = "PMO", c = 3, n = 50, it.max = 1000, 
                       scale = TRUE, seed = 100)

Arguments

M	matrix of data: rows are variables and columns are examples
dim	subspace dimension
pmethod	projection method. It must be one of the following: "RS" (random subspace projection) "PMO" (Plus Minus One random projection) "Norm" (normal random projection) "Achlioptas" (Achlioptas random projection)
c	number of clusters
n	number of RS projections
it.max	maximum number of iteration of the k-means algorithm (default 1000)
scale	if TRUE randomized projections are scaled (default)
seed	numerical seed for the random generator

Value

a list of the n clusterings. Each clustering is a list of vectors, and each vector represents a single cluster. The elements of the vectors are integers that corresponds to the number of the columns (examples) of the matrix M of the data.

Author(s)

Giorgio Valentini [e-mail]

Examples

# Multiple (20) k-means clusterings using Normal projections. 
M <- generate.sample0(n=10, m=2, sigma=2, dim=800)
l.norm <- Multiple.Random.kmeans (M, dim=100, pmethod="Norm", c=3, n=20)
# The same as above, using Random Subspace projections.
l.RS <-  Multiple.Random.kmeans (M, dim=100, pmethod="RS", c=3,  n=20)
# The same as above, using PMO projections, but with the number of clusters set to 5
l.RS.PMO <-  Multiple.Random.kmeans (M, dim=100, pmethod="PMO", c=5, n=20)

clusterv documentation built on June 8, 2025, 10:21 a.m.