Package: UAHDataScienceUC 1.0.1
Andriy Protsak Protsak
UAHDataScienceUC: Learn Clustering Techniques Through Examples and Code
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) <doi: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:
UAHDataScienceUC_1.0.1.tar.gz
UAHDataScienceUC_1.0.1.tar.gz(r-4.5-noble)UAHDataScienceUC_1.0.1.tar.gz(r-4.4-noble)
UAHDataScienceUC_1.0.1.tgz(r-4.4-emscripten)UAHDataScienceUC_1.0.1.tgz(r-4.3-emscripten)
UAHDataScienceUC.pdf |UAHDataScienceUC.html✨
UAHDataScienceUC/json (API)
NEWS
| # Install 'UAHDataScienceUC' in R: |
| install.packages('UAHDataScienceUC', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 20 days agofrom:11f35304c9. Checks:2 OK. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Feb 17 2025 |
| R-4.5-linux | OK | Feb 17 2025 |
Exports:agglomerative_clusteringcorrelation_clusteringdbscandivisive_clusteringgaussian_mixturegenetic_kmeanskmeans_
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Agglomerative Hierarchical Clustering | agglomerative_clustering |
| Hierarchical Correlation Clustering | correlation_clustering |
| Test Database 1 | db1 |
| Test Database 2 | db2 |
| Test Database 3 | db3 |
| Test Database 4 | db4 |
| Test Database 5 | db5 |
| Test Database 6 | db6 |
| Density Based Spatial Clustering of Applications with Noise (DBSCAN) | dbscan |
| Divisive Hierarchical Clustering | divisive_clustering |
| Gaussian mixture model | gaussian_mixture |
| Genetic K-Means Clustering | genetic_kmeans |
| Allele mutation probability computation | gka_allele_mutation |
| Centroid computation | gka_centers |
| Chromosome fixing method | gka_chromosome_fix |
| Crossover method i.e. K-Means Operator | gka_crossover |
| Fitness function | gka_fitness |
| Initialization method | gka_initialization |
| Mutation method | gka_mutation |
| Selection method | gka_selection |
| Total Within Cluster Variation (TWCV) computation | gka_twcv |
| K-Means Clustering | kmeans_ |
