Assessment of Cluster Stability by Randomized Maps


[Up] [Top]

Documentation for package ‘clusterv’ version 1.1.1

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.