Learn Clustering Techniques Through Examples and Code
Overview
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.
- Reseting old par settings after using helper function in divisive_clustering
- Updating DESCRIPTION file with acronyms meanings
From NEWS.md in the release
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Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK14 OK2026-04-18
- ERROR13 OK, 1 ERROR2026-04-16
- OK14 OK2026-04-12
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- ERROR13 OK, 1 ERROR2026-04-05
- OK14 OK2026-03-10
Documentation
- Help pages
- 22
- Examples that run
- 100%7 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 0%
Downloads
Dependencies
Code & Tests
Datasets
People & History
Pick two releases to compare their code metrics.
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- 1.0.1Latest
- 1.0.02025-02-17
- RR 4.4.0 released · 2024-04-24
Package metadata
- First published
- 2025-02-17
- Total releases
- 2 / 1 yrs
- License
- MIT + file LICENSE OSI
- Minimum R
- ≥ 4.3.0
- Bundled data
- 48 KB / 6 files
- Download size
- 118 KB
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