Kmeans.sim.projection: Function to compute similarity indices using random...

In mosclust: Model Order Selection for Clustering

Function to compute similarity indices using random projections and kmeans clustering.

Description

A vector of similarity measures between pairs of clusterings perturbed with random projections is computed for a given number of clusters. The dimension of the projected data, the type of randomized map and the similarity measure may be selected.

Usage

Kmeans.sim.projection(X, c = 2, nprojections = 100, dim = 2, pmethod = "PMO", 
  scale = TRUE, seed = 100, s = sFM, distance = "euclidean", hmethod = "ward.D")

Arguments

X	matrix of data (variables are rows, examples columns)
c	number of clusters
nprojections	number of pairs of projected data
dim	dimension of the projected data
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)
scale	if TRUE randomized projections are scaled (default)
seed	numerical seed for the random generator
s	similarity function to be used. It may be one of the following: - sFM (Fowlkes and Mallows) - sJaccard (Jaccard) - sM (matching coefficient) (default Fowlkes and Mallows)
distance	actually only the euclidean distance is available "euclidean" (default)
hmethod	parameter used for internal compatibility.

Value

vector of the computed similarity measures (length equal to nprojections)

Author(s)

Giorgio Valentini [e-mail]

See Also

Kmeans.sim.resampling, Kmeans.sim.noise

Examples

library("clusterv")
# Synthetic data set generation
M <- generate.sample6 (n=20, m=10, dim=600, d=3, s=0.2);
# computing a vector of similarity indices with 2 clusters:
v2 <- Kmeans.sim.projection(M, c = 2, nprojections = 20, dim = 200, 
                            pmethod = "PMO", s = sFM)
# computing a vector of similarity indices with 3 clusters:
v3 <- Kmeans.sim.projection(M, c = 3, nprojections = 20, dim = 200, 
                            pmethod = "PMO", s = sFM)
# computing a vector of similarity indices with 2 clusters using the Jaccard index
v2J <- Kmeans.sim.projection(M, c = 2, nprojections = 20, dim = 200, 
                             pmethod = "PMO", s = sJaccard)

mosclust documentation built on June 8, 2025, 11:23 a.m.