gka_fitness

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 

gka_fitness function

<dl><dt>twcv</dt>
<dd>a vector of size <code>p</code> with the total within cluster
variation of each individual in the population.</dd></dl>

Arguments

Eduardo Ruiz Sabajanes, <a href="/link/eduardo.ruizs%40edu.uah.es?package=UAHDataScienceUC&version=1.0.1" data-mini-rdoc="UAHDataScienceUC::eduardo.ruizs@edu.uah.es">eduardo.ruizs@edu.uah.es</a>

Author

Fitness function — gka_fitness

<dl>

<dt>twcv</dt>
<dd>a vector of size <code>p</code> with the total within cluster
variation of each individual in the population.</dd>

</dl>

Eduardo Ruiz Sabajanes, <a href='mailto:eduardo.ruizs@edu.uah.es'>eduardo.ruizs@edu.uah.es</a>

gka_fitness: Fitness function

Description

Usage

Value

Arguments

Author