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

Cristina Y. Morimoto, A. Pozo, M. D. Souto
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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.
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进化多目标聚类中目标函数的可接受性分析
在进化多目标聚类方法(EMOCs)中,各种聚类标准被用作目标函数。然而,大多数emoc并没有提供关于目标函数的选择和使用的详细分析。为了更好地选择和定义emoc中的目标,本文通过研究搜索方向及其在寻找最优结果中的潜力,提出了进化优化中聚类标准的可接受性分析。因此,我们证明了目标函数的可容许性如何影响优化。此外,我们还提供了关于emoc中聚类标准的组合和使用的见解。
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