ABSTRACT A multi-objective algorithm is always favoured over a single objective algorithm as it considers different aspects of a dataset in the form of various objectives. In this article, a multi-objective clustering algorithm has been proposed based on Differential Evolution. Here, three objectives have been considered to handle different complex datasets. In addition to this, a kernel function is hybridised with the objectives to evaluate the data on a hyperspace for reducing the impact of nonlinearity on cluster formation. Moreover, to get the best compromised solution from the Pareto front an effective fuzzy concept has been followed. Five metaheuristic approaches have been taken into consideration for performance comparison. These methodologies have been applied to twelve datasets and the result reveals the efficacy of the proposed model in data clustering.

中文翻译：

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多目标聚类：使用差分进化的基于内核的方法

摘要 多目标算法总是优于单目标算法，因为它以各种目标的形式考虑数据集的不同方面。本文提出了一种基于差分进化的多目标聚类算法。在这里，考虑了三个目标来处理不同的复杂数据集。除此之外，核函数与评估超空间数据的目标相结合，以减少非线性对集群形成的影响。此外，为了从帕累托前沿获得最佳折衷解决方案，遵循了一个有效的模糊概念。已经考虑了五种元启发式方法来进行性能比较。