Package: mosclust 1.0.2

Jessica Gliozzo

mosclust: Model Order Selection for Clustering

Stability based methods for model order selection in clustering problems (Valentini, G (2007), <doi:10.1093/bioinformatics/btl600>). Using multiple perturbations of the data the stability of clustering solutions is assessed. Different perturbations may be used: resampling techniques, random projections and noise injection. Stability measures for the estimate of clustering solutions and statistical tests to assess their significance are provided.

Authors:Giorgio Valentini [aut], Jessica Gliozzo [cre]

mosclust_1.0.2.tar.gz
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mosclust_1.0.2.tgz(r-4.6-any)mosclust_1.0.2.tgz(r-4.5-any)
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manual.pdf |manual.html✨
card.svg |card.png
mosclust/json (API)

# Install 'mosclust' in R:
install.packages('mosclust', repos = c('https://anacletolab.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/anacletolab/mosclust/issues

On CRAN:

Conda:

2.00 score 552 downloads 37 exports 3 dependencies

Last updated from:d605c3e4be. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK145
source / vignettesOK137
linux-release-x86_64OK140
macos-release-arm64OK160
macos-oldrel-arm64OK191
windows-develOK121
windows-releaseOK114
windows-oldrelOK117
wasm-releaseOK78

Exports:Bernstein.compute.pvaluesBernstein.ind.compute.pvaluesBernstein.p.valueChi.square.compute.pvaluesCompute.Chi.sqcompute.cumulative.multiplecompute.integralcompute.integral.from.similaritycumulative.valuesDo.boolean.membership.matrixdo.similarity.noisedo.similarity.projectiondo.similarity.resamplingFuzzy.kmeans.sim.noiseFuzzy.kmeans.sim.projectionFuzzy.kmeans.sim.resamplingHierarchical.sim.noiseHierarchical.sim.projectionHierarchical.sim.resamplingHybrid.testingHypothesis.testingIntersectKmeans.sim.noiseKmeans.sim.projectionKmeans.sim.resamplingPAM.sim.noisePAM.sim.projectionPAM.sim.resamplingperturb.by.noiseplot_cumulativeplot_cumulative.multipleplot_hist.similarityplot_multiple.hist.similarityplot_pvaluessFMsJaccardsM

Dependencies:clusterclustervMASS

Readme and manuals

Help Manual

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