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clusterv (version 1.1.1)

Assessment of Cluster Stability by Randomized Maps

Description

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), .

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Install

install.packages('clusterv')

Monthly Downloads

112

Version

1.1.1

License

GPL (>= 2)

Maintainer

Jessica Gliozzo

Last Published

May 14th, 2025

Functions in clusterv (1.1.1)

Cluster.validity

Validity indices computation
Generate.clusters

Multiple clusterings generation from the corresponding trees
Do.similarity.matrix

Functions to compute a pairwise similarity matrix.
Plus.Minus.One.random.projection

Plus-Minus-One (PMO) random projections
Random.kmeans.validity

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.
RS.hclustering

Multiple Hierarchical clusterings using RS random projections
Multiple.Random.hclustering

Multiple Random hierarchical clustering
Multiple.Random.kmeans

Multiple Random k-means clustering
generate.sample7

Sample7 generator: multivariate normally distributed data synthetic generator
generate.sample5

Sample5 generator of synthetic data
generate.uniform.random

Uniform bidimensional random data generator.
generate.uniform

Uniform bidimensional data generator
generate.sample.h3

Two-levels hierarchical cluster generator.
generate.sample3

Sample3 generator of synthetic data
generate.sample0

Sample0 generator of synthetic data
Norm.hclustering

Multiple Hierarchical clusterings using Normal random projections
generate.sample.h2

Three-level hierarchical cluster generator.
Transform.vector.to.list

Vector to list transformation of cluster representation
generate.sample1

Sample1 generator of synthetic data
PMO.hclustering

Multiple Hierarchical clusterings using Plus Minus One (PMO) random projections
generate.sample.h1

Two-levels hierarchical cluster generator.
generate.sample4

Sample4 generator of synthetic data
Random.PAM.validity

PAM clustering and validity indices computation using random projections of data
Random.fuzzy.kmeans.validity

Fuzzy-k-means clustering and validity indices computation using random projections of data
Validity.indices

Function to compute the validity index of each cluster.
generate.sample2

Sample2 generator of synthetic data
random.subspace

Random Subspace (RS) projections
generate.sample6

Sample6 generator: multivariate normally distributed data synthetic generator
rand.norm

Random generation of normal distributed data
norm.random.projection

Normal random projections
random.component.selection

Function to randomly select the indices of the variables selected by the random subspace projection
JL.predict.dim

Dimension of the subspace or the distortion predicted according to the Johnson Lindenstrauss lemma
Achlioptas.random.projection

Achlioptas random projection
Multiple.Random.PAM

Multiple Random PAM clustering
AC.index

Assignment Confidence (AC) index
Max.Expansion

Distortion measures: Max., min, and average expansion and contraction
Multiple.Random.fuzzy.kmeans

Multiple Random fuzzy-k-means clustering
Achlioptas.hclustering

Multiple Hierarchical clusterings using Achlioptas random projections