R: Assessment of Cluster Stability by Randomized Maps

# Assessment of Cluster Stability by Randomized Maps

## Documentation for package ‘clusterv’ version 1.1.1

- DESCRIPTION file
- User guides, package vignettes and other documentation

## Help Pages

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