Assessment of Cluster Stability by Randomized Maps

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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.