This package is considered a duplicate. The official version of this package is found at:https://anacletolab.r-universe.dev/clusterv

Package: clusterv 1.1.2

clusterv: Assessment of Cluster Stability by Randomized Maps

The reliability of clusters is estimated using random projections. A set of stability measures is provided to assess the reliability of the clusters discovered by a generic clustering algorithm. The stability measures are taylored to high dimensional data (e.g. DNA microarray data) (Valentini, G (2005), <doi:10.1093/bioinformatics/bti817>.

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

clusterv_1.1.2.tar.gz
clusterv_1.1.2.zip(r-4.7-any)clusterv_1.1.2.zip(r-4.6-any)clusterv_1.1.2.zip(r-4.5-any)
clusterv_1.1.2.tgz(r-4.6-any)clusterv_1.1.2.tgz(r-4.5-any)
clusterv_1.1.2.tar.gz(r-4.7-any)clusterv_1.1.2.tar.gz(r-4.6-any)
clusterv_1.1.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
clusterv/json (API)

# Install 'clusterv' in R:

install.packages('clusterv', repos = c('https://gliozzoj.r-universe.dev', 'https://cloud.r-project.org'))

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

On CRAN:

3.48 score 1 packages 408 downloads 54 exports 2 dependencies

Last updated from:1c355a6a06. Checks:9 OK. Indexed: no.

Target	Result	Time
linux-devel	OK	100
source / vignettes	OK	143
linux-release	OK	100
macos-release	OK	98
macos-oldrel	OK	116
windows-devel	OK	75
windows-release	OK	70
windows-oldrel	OK	83
wasm-release	OK	73

Exports:AC.index Achlioptas.hclustering Achlioptas.hclustering.tree Achlioptas.random.projection Average.Contraction Average.Expansion Cluster.validity Cluster.validity.from.similarity Do.similarity.matrix Do.similarity.matrix.partition Generate.clusters generate.sample.h1 generate.sample.h2 generate.sample.h3 generate.sample0 generate.sample1 generate.sample2 generate.sample3 generate.sample4 generate.sample5 generate.sample6 generate.sample7 generate.uniform generate.uniform.random JL.predict.dim JL.predict.dim.multiple JL.predict.distortion Max.Contraction Max.Expansion Max.Min.Contraction Max.Min.Expansion Min.Expansion Multiple.Random.fuzzy.kmeans Multiple.Random.hclustering Multiple.Random.kmeans Multiple.Random.PAM Norm.hclustering Norm.hclustering.tree norm.random.projection Plus.Minus.One.random.projection PMO.hclustering PMO.hclustering.tree rand.norm.generate rand.norm.generate.full random.component.selection Random.fuzzy.kmeans.validity Random.hclustering.validity Random.kmeans.validity Random.PAM.validity random.subspace RS.hclustering RS.hclustering.tree Transform.vector.to.list Validity.indices

Dependencies:cluster MASS

clusterv tutorial

Introduction | Overview of the clusterv R package | Main functionalities implemented in clusterv | Getting started with Clusterv | Generation of randomly projected data | Computation of the distortion induced by random projections | Estimate of the reliability of clusters generated by specific clustering algorithms | Estimate of the reliability of clusters generated by a generic clustering algorithm | An applicative example to the analysis of DNA microarray data: analysis of cluster reliability in lung tumor patients

Rendered fromclusterv-tutorial.pdf.asisusingR.rsp::asis

Last update: 2025-05-08
Started: 2025-05-08

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Assignment Confidence (AC) index	AC.index
Multiple Hierarchical clusterings using Achlioptas random projections	Achlioptas.hclustering Achlioptas.hclustering.tree
Achlioptas random projection	Achlioptas.random.projection
Validity indices computation	Cluster.validity Cluster.validity.from.similarity
Functions to compute a pairwise similarity matrix.	Do.similarity.matrix Do.similarity.matrix.partition
Multiple clusterings generation from the corresponding trees	Generate.clusters
Two-levels hierarchical cluster generator.	generate.sample.h1
Three-level hierarchical cluster generator.	generate.sample.h2
Two-levels hierarchical cluster generator.	generate.sample.h3
Sample0 generator of synthetic data	generate.sample0
Sample1 generator of synthetic data	generate.sample1
Sample2 generator of synthetic data	generate.sample2
Sample3 generator of synthetic data	generate.sample3
Sample4 generator of synthetic data	generate.sample4
Sample5 generator of synthetic data	generate.sample5
Sample6 generator: multivariate normally distributed data synthetic generator	generate.sample6
Sample7 generator: multivariate normally distributed data synthetic generator	generate.sample7
Uniform bidimensional data generator	generate.uniform
Uniform bidimensional random data generator.	generate.uniform.random
Dimension of the subspace or the distortion predicted according to the Johnson Lindenstrauss lemma	JL.predict.dim JL.predict.dim.multiple JL.predict.distortion
Distortion measures: Max., min, and average expansion and contraction	Average.Contraction Average.Expansion Max.Contraction Max.Expansion Max.Min.Contraction Max.Min.Expansion Min.Expansion
Multiple Random fuzzy-k-means clustering	Multiple.Random.fuzzy.kmeans
Multiple Random hierarchical clustering	Multiple.Random.hclustering
Multiple Random k-means clustering	Multiple.Random.kmeans
Multiple Random PAM clustering	Multiple.Random.PAM
Multiple Hierarchical clusterings using Normal random projections	Norm.hclustering Norm.hclustering.tree
Normal random projections	norm.random.projection
Plus-Minus-One (PMO) random projections	Plus.Minus.One.random.projection
Multiple Hierarchical clusterings using Plus Minus One (PMO) random projections	PMO.hclustering PMO.hclustering.tree
Random generation of normal distributed data	rand.norm.generate rand.norm.generate.full
Function to randomly select the indices of the variables selected by the random subspace projection	random.component.selection
Fuzzy-k-means clustering and validity indices computation using random projections of data	Random.fuzzy.kmeans.validity
Random hierarchical clustering and validity index computation using random projections of data.	Random.hclustering.validity
k-means clustering and validity indices computation using random projections of data	Random.kmeans.validity
PAM clustering and validity indices computation using random projections of data	Random.PAM.validity
Random Subspace (RS) projections	random.subspace
Multiple Hierarchical clusterings using RS random projections	RS.hclustering RS.hclustering.tree
Vector to list transformation of cluster representation	Transform.vector.to.list
Function to compute the validity index of each cluster.	Validity.indices