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) . 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.