UAHDataScienceUC (version 1.0.1)
Learn Clustering Techniques Through Examples and Code
Description
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) .
Includes educational datasets highlighting different clustering challenges, based on
'scikit-learn' examples (Pedregosa et al., 2011)
. Features detailed
algorithm explanations, visualizations, and weighted distance calculations for
enhanced learning.