Installation | Available algorithms | K-Means Clustering | Agglomerative Hierarchical Clustering | DBSCAN | Gaussian Mixture Models | Genetic K-Means | Correlation Clustering | Distances
Package: UAHDataScienceUC 1.0.1
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.zip(r-4.7)UAHDataScienceUC_1.0.1.zip(r-4.6)UAHDataScienceUC_1.0.1.zip(r-4.5)
UAHDataScienceUC_1.0.1.tgz(r-4.6-any)UAHDataScienceUC_1.0.1.tgz(r-4.5-any)
UAHDataScienceUC_1.0.1.tar.gz(r-4.7-any)UAHDataScienceUC_1.0.1.tar.gz(r-4.6-any)
UAHDataScienceUC_1.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
UAHDataScienceUC/json (API)
| # Install 'UAHDataScienceUC' in R: |
| install.packages('UAHDataScienceUC', repos = c('https://andriyprotsak5.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/andriyprotsak5/uahdatascienceuc/issues
Datasets:
Last updated from:a4c1398e73. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 126 | ||
| source / vignettes | OK | 163 | ||
| linux-release-x86_64 | OK | 113 | ||
| macos-release-arm64 | OK | 82 | ||
| macos-oldrel-arm64 | OK | 113 | ||
| windows-devel | OK | 85 | ||
| windows-release | OK | 86 | ||
| windows-oldrel | OK | 72 | ||
| wasm-release | OK | 95 |
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_ |
