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Cluster validity indices for automatic clustering: A comprehensive review.

Abiodun M Ikotun¹, Faustin Habyarimana¹, Absalom E Ezugwu²

¹School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, King Edward Avenue, Pietermaritzburg Campus, Pietermaritzburg, 3201, KwaZulu-Natal, South Africa.

|February 3, 2025

Summary

This summary is machine-generated.

This study reviews cluster validity indices (CVIs) used in metaheuristic clustering. It identifies and analyzes CVIs to help researchers select the best ones for optimal algorithm performance.

Keywords:

Automatic clustering Cluster validity index Clustering Metaheuristic algorithms Optimization algorithms

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Area of Science:

Data Science
Artificial Intelligence
Machine Learning

Background:

Cluster Validity Indices (CVIs) are crucial for evaluating clustering algorithm performance.
Existing CVIs vary in their mathematical models and suitability for different clustering tasks.
Metaheuristic algorithms rely on CVIs as fitness functions for optimization.

Purpose of the Study:

To systematically review CVIs used as fitness functions in metaheuristic-based automatic clustering algorithms.
To analyze the characteristics and performance of various CVIs.
To guide researchers in selecting appropriate CVIs for specific applications.

Main Methods:

Systematic literature review of CVIs in metaheuristic clustering.
Experimental evaluation of common CVIs using synthetic and real-world datasets.
Utilized the SOSK-means automatic clustering algorithm for experiments.

Main Results:

Identified and analyzed a range of CVIs employed in metaheuristic clustering.
Demonstrated varying performance of CVIs across different datasets.
Provided insights into the strengths and weaknesses of common CVIs.

Conclusions:

A comprehensive understanding of CVIs is essential for effective metaheuristic clustering.
The choice of CVI significantly impacts the performance of automatic clustering algorithms.
This review offers a valuable resource for selecting optimal CVIs in diverse application domains.