Multiple.Random.hclustering: Multiple Random hierarchical clustering

In clusterv: Assessment of Cluster Stability by Randomized Maps

Multiple Random hierarchical clustering

Description

Multiple Random hierarchical 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.hclustering(M, dim, pmethod = "RS", c = 3, hmethod = "average", 
                            n = 50, scale = TRUE, seed = 100, distance="euclidean")

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
hmethod	the agglomeration method to be used. This should be one of "ward.D", "single", "complete", "average", "mcquitty", "median" or "centroid", according to the hclust method of the package stats.
n	number of random projections
scale	if TRUE (default) the random projections are scaled
seed	numerical seed for the random generator
distance	it must be one of the two: "euclidean" (default) or "pearson" (that is 1 - Pearson correlation)

Value

a list of the n clusterings obtained by randomized hierarchical clustering. Each clustering is a list vector, 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]

See Also

Achlioptas.random.projection, Plus.Minus.One.random.projection,

norm.random.projection,random.subspace

Examples

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

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