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 DOI: 10.32614/CRAN.package.clustlearn Author: Eduardo Ruiz Sabajanes [aut, cre], Juan Jose Cuadrado Gallego [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.html , clustlearn.pdf Downloads: Package source: clustlearn_1.0.0.tar.gz Windows binaries: r-devel: clustlearn_1.0.0.zip, r-release: clustlearn_1.0.0.zip, r-oldrel: clustlearn_1.0.0.zip macOS binaries: r-release (arm64): clustlearn_1.0.0.tgz, r-oldrel (arm64): clustlearn_1.0.0.tgz, r-release (x86_64): clustlearn_1.0.0.tgz, r-oldrel (x86_64): clustlearn_1.0.0.tgz Linking:Please use the canonical form https://CRAN.R-project.org/package=clustlearn to link to this page.
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