Learn Clustering Techniques Through Examples and Code R package UAHDataScienceUC version 1.0.1 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. Software Depends: R (>= 4.3.0) Imports: proxy (>= 0.4-27), cli (>= 3.6.1) Suggests: deldir (>= 1.0-9), knitr, rmarkdown Andriy Protsak Protsak <andriy.protsak@edu.uah.es> Comprehensive R Archive Network (CRAN) Eduardo Ruiz Sabajanes [aut], Roberto Alcantara [aut], Juan Jose Cuadrado Gallego [aut] (ORCID: <https://orcid.org/0000-0001-8178-5556>), Andriy Protsak Protsak [aut, cre], Universidad de Alcala [cph] MIT + file LICENSE (https://CRAN.R-project.org/package=UAHDataScienceUC/LICENSE) 2025-02-17 application/tgz https://CRAN.R-project.org/package=UAHDataScienceUC doi:10.32614/CRAN.package.UAHDataScienceUC