Model Order Selection for Clustering

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Documentation for package ‘mosclust’ version 1.0.2

• DESCRIPTION file.
• User guides, package vignettes and other documentation.

Help Pages

mosclust-package	Model order selection for clustering
Bernstein.compute.pvalues	Function to compute the stability indices and the p-values associated to a set of clusterings according to Bernstein inequality.
Bernstein.ind.compute.pvalues	Function to compute the stability indices and the p-values associated to a set of clusterings according to Bernstein inequality.
Bernstein.p.value	Function to compute the p-value according to Bernstein inequality.
Chi.square.compute.pvalues	Function to compute the stability indices and the p-values associated to a set of clusterings according to the chi-square test between multiple proportions.
Compute.Chi.sq	Function to evaluate if a set of similarity distributions significantly differ using the chi square test.
compute.cumulative.multiple	Function to compute the empirical cumulative distribution function (ECDF) of the similarity measures.
compute.integral	Functions to compute the integral of the ecdf of the similarity values
compute.integral.from.similarity	Functions to compute the integral of the ecdf of the similarity values
cumulative.values	Function to compute the empirical cumulative distribution function (ECDF) of the similarity measures.
Do.boolean.membership.matrix	Function to compute and build up a pairwise boolean membership matrix.
do.similarity.noise	Function that computes sets of similarity indices using injection of gaussian noise.
do.similarity.projection	Function that computes sets of similarity indices using randomized maps.
do.similarity.resampling	Function that computes sets of similarity indices using resampling techniques.
Fuzzy.kmeans.sim.noise	Function to compute similarity indices using noise injection techniques and fuzzy c-mean clustering.
Fuzzy.kmeans.sim.projection	Function to compute similarity indices using random projections and fuzzy c-mean clustering.
Fuzzy.kmeans.sim.resampling	Function to compute similarity indices using resampling techniques and fuzzy c-mean clustering.
Hierarchical.sim.noise	Function to compute similarity indices using noise injection techniques and hierarchical clustering.
Hierarchical.sim.projection	Function to compute similarity indices using random projections and hierarchical clustering.
Hierarchical.sim.resampling	Function to compute similarity indices using resampling techniques and hierarchical clustering.
Hybrid.testing	Statistical test based on stability methods for model order selection.
Hypothesis.testing	Function to select significant clusterings from a given set of p-values
Intersect	Function to compute the intersection between elements of two vectors
Kmeans.sim.noise	Function to compute similarity indices using noise injection techniques and kmeans clustering.
Kmeans.sim.projection	Function to compute similarity indices using random projections and kmeans clustering.
Kmeans.sim.resampling	Function to compute similarity indices using resampling techniques and kmeans clustering.
mosclust	Model order selection for clustering
PAM.sim.noise	Function to compute similarity indices using noise injection techniques and PAM clustering.
PAM.sim.projection	Function to compute similarity indices using random projections and PAM clustering.
PAM.sim.resampling	Function to compute similarity indices using resampling techniques and PAM clustering.
perturb.by.noise	Function to generate a data set perturbed by noise.
plot_cumulative	Function to plot the empirical cumulative distribution function of the similarity values
plot_cumulative.multiple	Function to plot the empirical cumulative distribution function of the similarity values
plot_hist.similarity	Plotting histograms of similarity measures between clusterings
plot_multiple.hist.similarity	Plotting histograms of similarity measures between clusterings
plot_pvalues	Function to plot p-values for different tests of hypothesis
sFM	Similarity measures between pairs of clusterings
sJaccard	Similarity measures between pairs of clusterings
sM	Similarity measures between pairs of clusterings