Fuzzy.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 fuzzy c-mean 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 fuzzy c-mean algorithm. The fraction of the resampled data (without replacement) and the similarity measure can be selected.

Usage

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

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

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