do.similarity.noise: Function that computes sets of similarity indices using...

In mosclust: Model Order Selection for Clustering

Function that computes sets of similarity indices using injection of gaussian noise.

Description

This function may use different clustering algorithms and different similarity measures to compute similarity indices. Injection of gaussian noise is applied to perturb the data. The gaussian noise added to the data has 0 mean and the standard deviation is estimated from the data (it is set to a given percentile value of the standard deviations computed for each variable). More precisely pairs of data sets are perturbed with noise and then are clustered and the resulting clusterings are compared using similarity indices between pairs of clusterings (e.g. Rand Index, Jaccard or Fowlkes and Mallows indices). These indices are computed multiple times for different number of clusters.

Usage

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

Arguments

X	matrix of data (variables are rows, examples columns)
c	if it is a vector of length 1, number of clusters from 2 to c are considered; otherwise are considered the number of clusters stored in the vector c.
nnoisy	number of pairs of noisy data
perc	percentile of the standard deviations to be used for the added gaussian noise (default: median)
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)
alg.clust.sim	method that computes the similarity indices using subsampling techniques and a specific clustering algorithm. It may be one of the following: - Hierarchical.sim.resampling (hierarchical clustering algorithm, default) - Kmeans.sim.resampling (c - mean algorithm) - PAM.sim.resampling (Prediction Around Medoid algorithm) - Fuzzy.kmeans.sim.resampling (Fuzzy c-mean)
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

a matrix that stores the similarity between pairs of clustering across multiple number of clusters and multiple clusterings performed on subsamples of the original data. Number of rows equal to the length of c (number of clusters); number of columns equal to nsub, that is the number of subsamples considered for each number of clusters.

Author(s)

Giorgio Valentini [e-mail]

References

McShane, L.M., Radmacher, D., Freidlin, B., Yu, R., Li, M.C. and Simon, R., Method for assessing reproducibility of clustering patterns observed in analyses of microarray data, Bioinformatics, 11(8), pp. 1462-1469, 2002.

See Also

do.similarity.projection, do.similarity.resampling

Examples

library("clusterv")
# Data set generation
M <- generate.sample6 (n=20, m=10, dim=600, d=3, s=0.2);
# computing similarity indices with the fuzzy c-mean algorithm
Sn.Fuzzy.kmeans.sample6 <- do.similarity.noise(M, c=8, nnoisy=30, perc=0.5, s=sFM, 
                                      alg.clust.sim=Fuzzy.kmeans.sim.noise);
# computing similarity indices using the c-mean algorithm
Sn.Fuzzy.kmeans.sample6 <- do.similarity.noise(M, c=8, nnoisy=30, perc=0.5, s=sFM, 
                                      alg.clust.sim=Fuzzy.kmeans.sim.noise);
# computing similarity indices using the hierarchical clustering algorithm
Sn.HC.sample6 <- do.similarity.noise(M, c=8, nnoisy=30, perc=0.5, s=sFM, 
                                      alg.clust.sim=Hierarchical.sim.noise);

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