clusterv (version 1.1.1)

Multiple.Random.PAM: Multiple Random PAM clustering

Description

Multiple Random Partition Around Medoids (PAM) clusterings are computed using random projections of data. The pam function of the package cluster is used as implementation of the base PAM algorithm. 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.PAM(M, dim, pmethod = "PMO", c = 3, n = 50, scale = TRUE, 
                    seed = -1, distance = "euclidean")

Value

a list of the n clusterings obtained by the PAM algorithm clustering. 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.

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

scale

if TRUE randomized projections are scaled (default)

seed

numerical seed for the random generator

distance

it must be one of the two: "euclidean" (default) or "pearson" (that is 1 - Pearson correlation)

Author

Giorgio Valentini

Examples

# Multiple (20) PAM clusterings using Normal projections. 
M <- generate.sample0(n=10, m=2, sigma=2, dim=800)
l.norm <- Multiple.Random.PAM (M, dim=100, pmethod="Norm", c=3, n=20)
# The same as above, using Random Subspace projections.
l.RS <-  Multiple.Random.PAM (M, dim=100, pmethod="RS", c=3,  n=20)
# The same as above, using PMO projections, but with the number of clusters set to 7
l.RS.PMO <-  Multiple.Random.PAM (M, dim=100, pmethod="PMO", c=7, n=20)