datasets

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.

Andriy Protsak Protsak

UAHDataScienceUC

Learn Clustering Techniques Through Examples and Code

Eduardo Ruiz Sabajanes

Roberto Alcantara

Juan Jose Cuadrado Gallego

Universidad de Alcala 

db2 function


<div class="section" id="db-">
<h3><code>db2</code></h3><p></p><p>
</p><p>A data frame with 500 rows and 2 columns.</p>
<p>The data points form two moons.</p>
</div>


Format

Test Database 2 — db2


<div class='section' id='db-'>
<h3><code>db2</code></h3>


<p>A data frame with 500 rows and 2 columns.</p>
<p>The data points form two moons.</p>
</div>


Test Database 2

db2: Test Database 2

Description

Usage

Arguments

Format

`db2`