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cff-version: 1.2.0
message: 'To cite package "UAHDataScienceUC" in publications use:'
type: software
license: MIT
title: 'UAHDataScienceUC: Learn Clustering Techniques Through Examples and Code'
version: 1.0.1
doi: 10.32614/CRAN.package.UAHDataScienceUC
abstract: 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) <https://doi.org/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.
authors:
- family-names: Ruiz Sabajanes
  given-names: Eduardo
  email: eduardo.ruizs@edu.uah.es
- family-names: Alcantara
  given-names: Roberto
  email: roberto.alcantara@edu.uah.es
- family-names: Cuadrado Gallego
  given-names: Juan Jose
  email: jjcg@uah.es
  orcid: https://orcid.org/0000-0001-8178-5556
- family-names: Protsak
  given-names: Andriy
  email: andriy.protsak@edu.uah.es
repository: https://andriyprotsak5.r-universe.dev
commit: a4c1398e730627b6e0b9fcd927dff526c5c35eb7
contact:
- family-names: Protsak
  given-names: Andriy
  email: andriy.protsak@edu.uah.es