Hierarchical.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 hierarchical 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, the similarity measure and the type of hierarchical clustering may be selected.

Usage

Hierarchical.sim.projection(X, c = 2, nprojections = 100, dim = 2, pmethod = "RS", 
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	it must be one of the two: "euclidean" (default) or "pearson" (that is 1 - Pearson correlation)
hmethod	the agglomeration method to be used. This parameter is used only by the hierarchical clustering algorithm. This should be one of the following: "ward.D", "single", "complete", "average", "mcquitty", "median" or "centroid", according of the hclust method of the package stats.

Value

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

Author(s)

Giorgio Valentini [e-mail]

See Also

Hierarchical.sim.resampling, Hierarchical.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 <- Hierarchical.sim.projection(M, c = 2, nprojections = 20, dim = 200, 
                                  pmethod = "PMO", s = sFM)
# computing a vector of similarity indices with 3 clusters:
v3 <- Hierarchical.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 <- Hierarchical.sim.projection(M, c = 2, nprojections = 20, dim = 200, 
                                   pmethod = "PMO", s = sJaccard)
#  2 clusters using the Jaccard index and Pearson correlation
v2JP <- Hierarchical.sim.projection(M, c = 2, nprojections = 20, dim = 200, 
                                    pmethod = "PMO", s = sJaccard, distance="pearson")

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