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drclust (version 0.1.1)

Simultaneous Clustering and (or) Dimensionality Reduction

Description

Methods for simultaneous clustering and dimensionality reduction such as: Double k-means, Reduced k-means, Factorial k-means, Clustering with Disjoint PCA but also methods for exclusively dimensionality reduction: Disjoint PCA, Disjoint FA. The statistical methods implemented refer to the following articles: de Soete G., Carroll J. (1994) "K-means clustering in a low-dimensional Euclidean space" ; Vichi M. (2001) "Double k-means Clustering for Simultaneous Classification of Objects and Variables" ; Vichi M., Kiers H.A.L. (2001) "Factorial k-means analysis for two-way data" ; Vichi M., Saporta G. (2009) "Clustering and disjoint principal component analysis" ; Vichi M. (2017) "Disjoint factor analysis with cross-loadings" .

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Version

Install

install.packages('drclust')

Monthly Downloads

177

Version

0.1.1

License

GPL (>= 3)

Maintainer

Ionel Prunila

Last Published

October 3rd, 2025

Functions in drclust (0.1.1)

silhouette

Silhouette
kaiserCrit

Selecting the number of principal components to be extracted from a dataset
doublekm

Double k-means Clustering
apseudoF

pseudoF (pF or Calinski-Harabsz) index for choosing k in partitioning models
CronbachAlpha

Cronbach Alpha
dispca

Disjoint Principal Components Analysis
dpcakm

Clustering with Disjoint Principal Components Analysis
disfa

Disjoint Factor Analysis
cluster

classification variable
centree

Ward-dendrogeam of centroids of partitioning models
redkm

k-means on a reduced subspace
mrand

Adjusted Rand Index
factkm

Factorial k-means
dpseudoF

double pseudoF (Calinski-Harabsz) index
heatm

Heatmap of a partition in a reduced subspace