UAHDataScienceUC: Learn Clustering Techniques Through Examples and Code

A comprehensive educational package combining clustering algorithms with detailed step-by-step explanations. Provides implementations of both traditional (hierarchical, k-means) and modern (Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Gaussian Mixture Models (GMM), genetic k-means) clustering methods as described in Ezugwu et. al., (2022) <doi:10.1016/j.engappai.2022.104743>. Includes educational datasets highlighting different clustering challenges, based on 'scikit-learn' examples (Pedregosa et al., 2011) <https://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html>. Features detailed algorithm explanations, visualizations, and weighted distance calculations for enhanced learning.

Version:	1.0.1
Depends:	R (≥ 4.3.0)
Imports:	proxy (≥ 0.4-27), cli (≥ 3.6.1)
Suggests:	deldir (≥ 1.0-9), knitr, rmarkdown
Published:	2025-02-17
DOI:	10.32614/CRAN.package.UAHDataScienceUC
Author:	Eduardo Ruiz Sabajanes [aut], Roberto Alcantara [aut], Juan Jose Cuadrado Gallego [aut], Andriy Protsak Protsak [aut, cre], Universidad de Alcala [cph]
Maintainer:	Andriy Protsak Protsak <andriy.protsak at edu.uah.es>
License:	MIT + file LICENSE
NeedsCompilation:	no
Materials:	NEWS
CRAN checks:	UAHDataScienceUC results

Documentation:

Reference manual:	UAHDataScienceUC.html , UAHDataScienceUC.pdf
Vignettes:	Using the Unified Interface in clustlearn 1.1.0 (source, R code)

Downloads:

Package source:	UAHDataScienceUC_1.0.1.tar.gz
Windows binaries:	r-devel: UAHDataScienceUC_1.0.1.zip, r-release: UAHDataScienceUC_1.0.1.zip, r-oldrel: UAHDataScienceUC_1.0.1.zip
macOS binaries:	r-release (arm64): UAHDataScienceUC_1.0.1.tgz, r-oldrel (arm64): UAHDataScienceUC_1.0.1.tgz, r-release (x86_64): UAHDataScienceUC_1.0.1.tgz, r-oldrel (x86_64): UAHDataScienceUC_1.0.1.tgz
Old sources:	UAHDataScienceUC archive

Linking:

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