KMEANS.KNN: KMeans and KNN Clustering Package

Implementation of Kmeans clustering algorithm and a supervised KNN (K Nearest Neighbors) learning method. It allows users to perform unsupervised clustering and supervised classification on their datasets. Additional features include data normalization, imputation of missing values, and the choice of distance metric. The package also provides functions to determine the optimal number of clusters for Kmeans and the best k-value for KNN: knn_Function(), find_Knn_best_k(), KMEANS_FUNCTION(), and find_Kmeans_best_k().

Version:	0.1.0
Imports:	factoextra, cluster, ggplot2, stats, assertthat, class, caret, grDevices
Suggests:	knitr, rmarkdown, testthat (≥ 3.0.0)
Published:	2024-05-17
DOI:	10.32614/CRAN.package.KMEANS.KNN
Author:	LALLOGO Lassané [aut, cre]
Maintainer:	LALLOGO Lassané <lassanelallogo2002 at gmail.com>
License:	GPL-3
NeedsCompilation:	no
Materials:	README
CRAN checks:	KMEANS.KNN results

Documentation:

Reference manual:	KMEANS.KNN.pdf
Vignettes:	myutils

Downloads:

Package source:	KMEANS.KNN_0.1.0.tar.gz
Windows binaries:	r-devel: KMEANS.KNN_0.1.0.zip, r-release: KMEANS.KNN_0.1.0.zip, r-oldrel: KMEANS.KNN_0.1.0.zip
macOS binaries:	r-release (arm64): KMEANS.KNN_0.1.0.tgz, r-oldrel (arm64): KMEANS.KNN_0.1.0.tgz, r-release (x86_64): KMEANS.KNN_0.1.0.tgz, r-oldrel (x86_64): KMEANS.KNN_0.1.0.tgz

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