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 |
| 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:
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