Kmeans.sim.resampling: Function to compute similarity indices using resampling...

In mosclust: Model Order Selection for Clustering

Function to compute similarity indices using resampling techniques and kmeans clustering.

Description

A vector of similarity measures between pairs of clusterings perturbed with resampling techniques is computed for a given number of clusters, using the kmeans algorithm. The fraction of the resampled data (without replacement) and the similarity measure can be selected.

Usage

Kmeans.sim.resampling(X, c = 2, nsub = 100, f = 0.8, s = sFM, 
                      distance = "euclidean", hmethod = NULL)

Arguments

X	matrix of data (variables are rows, examples columns)
c	number of clusters
nsub	number of subsamples
f	fraction of the data resampled without replacement
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 nsub)

Author(s)

Giorgio Valentini [e-mail]

See Also

Kmeans.sim.projection, 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.resampling(M, c = 2, nsub = 20, f = 0.8, s = sFM)
# computing a vector of similarity indices with 3 clusters:
v3 <- Kmeans.sim.resampling(M, c = 3, nsub = 20, f = 0.8, s = sFM)
# computing a vector of similarity indices with 2 clusters using the Jaccard index
v2J <- Kmeans.sim.resampling(M, c = 2, nsub = 20, f = 0.8, s = sJaccard)

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