Hierarchical.sim.noise: Function to compute similarity indices using noise injection...

In mosclust: Model Order Selection for Clustering

Function to compute similarity indices using noise injection techniques and hierarchical clustering.

Description

A vector of similarity measures between pairs of clusterings perturbed with random noise is computed for a given number of clusters. The variance of the added gaussian noise, estimated from the data as the perc percentile of the standard deviations of the input variables, the percentile itself, the similarity measure and the type of hierarchical clustering may be selected.

Usage

Hierarchical.sim.noise(X, c = 2, nnoisy = 100, perc = 0.5, s = sFM, 
                       distance = "euclidean", hmethod = "ward.D")

Arguments

X	matrix of data (variables are rows, examples columns)
c	number of clusters
nnoisy	number of pairs of noisy data
perc	percentile of the standard deviations to be used for the added gaussian noise (def. 0.5)
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 nnoisy)

Author(s)

Giorgio Valentini [e-mail]

See Also

Hierarchical.sim.projection, Hierarchical.sim.resampling, perturb.by.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.noise(M, c = 2, nnoisy = 20,  s = sFM)
# computing a vector of similarity indices with 3 clusters:
v3 <- Hierarchical.sim.noise(M, c = 3, nnoisy = 20,  s = sFM)
# computing a vector of similarity indices with 2 clusters using the Jaccard index
v2J <- Hierarchical.sim.noise(M, c = 2, nnoisy = 20,  s = sJaccard)
#  2 clusters using the Jaccard index and Pearson correlation
v2JP <- Hierarchical.sim.noise(M, c = 2, nnoisy = 20, s = sJaccard, distance="pearson")
# 2 clusters using 0.95 percentile (more noise)
v095 <- Hierarchical.sim.noise(M, c = 2, nnoisy = 20,  s = sFM, perc=0.95)

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