Fuzzy.kmeans.sim.noise: Function to compute similarity indices using noise injection...
In mosclust: Model Order Selection for Clustering

Fuzzy.kmeans.sim.noise

R Documentation

Function to compute similarity indices using noise injection techniques and fuzzy c-mean 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 and the similarity measure can be selected.

Usage

Fuzzy.kmeans.sim.noise(X, c = 2, nnoisy = 100, perc = 0.5, s = sFM, 
                       distance = "euclidean", hmethod = NULL)

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`	parameter used for internal compatibility.

Value

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

Author(s)

Giorgio Valentini valentini@di.unimi.it

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.noise(M, c = 2, nnoisy = 20,  s = sFM)
# computing a vector of similarity indices with 3 clusters:
v3 <- Fuzzy.kmeans.sim.noise(M, c = 3, nnoisy = 20,  s = sFM)
# computing a vector of similarity indices with 2 clusters using the Jaccard index
v2J <- Fuzzy.kmeans.sim.noise(M, c = 2, nnoisy = 20,  s = sJaccard)
# 2 clusters using 0.95 percentile (more noise)
v095 <- Fuzzy.kmeans.sim.noise(M, c = 2, nnoisy = 20,  s = sFM, perc=0.95)

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