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>.">

clustlearn: Learn Clustering Techniques Through Examples and Code (original) (raw)

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 GallegoORCID 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
URL: https://github.com/Ediu3095/clustlearn
NeedsCompilation: no
Materials: README NEWS
CRAN checks: clustlearn results

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