clustlearn: Learn Clustering Techniques Through Examples and Code

Clustering methods, which (if asked) can provide step-by-step explanations of the algorithms used, as described in Ezugwu et. al., (2022) <doi:10.1016/j.engappai.2022.104743>; and datasets to test them on, which highlight the strengths and weaknesses of each technique, as presented in the clustering section of 'scikit-learn' (Pedregosa et al., 2011) <https://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html>.

Version: 1.0.0
Depends: R (≥ 4.3.0)
Imports: proxy (≥ 0.4-27), cli (≥ 3.6.1)
Suggests: deldir (≥ 1.0-9)
Published: 2023-09-14
Author: Eduardo Ruiz Sabajanes [aut, cre], Juan Jose Cuadrado Gallego ORCID iD [ctb], Universidad de Alcala [cph]
Maintainer: Eduardo Ruiz Sabajanes <eduardo.ruizs at edu.uah.es>
BugReports: https://github.com/Ediu3095/clustlearn/issues
License: MIT + file LICENSE
URL: https://github.com/Ediu3095/clustlearn
NeedsCompilation: no
Materials: README NEWS
CRAN checks: clustlearn results

Documentation:

Reference manual: clustlearn.pdf

Downloads:

Package source: clustlearn_1.0.0.tar.gz
Windows binaries: r-prerel: clustlearn_1.0.0.zip, r-release: clustlearn_1.0.0.zip, r-oldrel: not available
macOS binaries: r-prerel (arm64): clustlearn_1.0.0.tgz, r-release (arm64): clustlearn_1.0.0.tgz, r-oldrel (arm64): not available, r-prerel (x86_64): clustlearn_1.0.0.tgz, r-release (x86_64): clustlearn_1.0.0.tgz

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