An Analysis of the Admissibility of the Objective Functions Applied in Evolutionary Multi-objective Clustering

TL;DR

This paper analyzes how the choice of objective functions affects the effectiveness of evolutionary multi-objective clustering methods, providing insights into their admissibility and influence on optimization outcomes.

Contribution

It offers a detailed analysis of the admissibility of clustering criteria in EMOCs, guiding better objective function selection and combination.

Findings

Admissibility of objective functions impacts search direction.

Certain criteria better guide the optimization process.

Insights into effective combinations of clustering objectives.

Abstract

A variety of clustering criteria has been applied as an objective function in Evolutionary Multi-Objective Clustering approaches (EMOCs). However, most EMOCs do not provide detailed analysis regarding the choice and usage of the objective functions. Aiming to support a better choice and definition of the objectives in the EMOCs, this paper proposes an analysis of the admissibility of the clustering criteria in evolutionary optimization by examining the search direction and its potential in finding optimal results. As a result, we demonstrate how the admissibility of the objective functions can influence the optimization. Furthermore, we provide insights regarding the combinations and usage of the clustering criteria in the EMOCs.