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This study delves into bio-inspired approaches and clustering methodologies to introduce an automated clustering algorithm named Multi-objective Clustering Algorithm (MCA). Using multi-objective strategies and several combination measures, this method calculates the optimal number of clusters and element partitioning by minimizing intra-clustering measures and maximizing inter-clustering ones. Through experimentation on three benchmark datasets, the results highlight the success of the MCA in obtaining a set of optimal solutions (Hybrid Pareto front) through the integration of multi-objective strategies and clustering measures. Moreover, the Dunn clustering validity index is used to support the decision maker in selecting the optimal solution among the ones presented in the Hybrid Pareto front. This approach allows decision-makers to choose the most suitable solution by incorporating additional insights beyond the model.

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A Multi-objective Clustering Algorithm Integrating Intra-Clustering and Inter-Clustering Measures

  • 会议论文

  • 发表日期：

    2025年

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  • Beatriz Flamia Azevedo,
  • Ana Maria A. C. Rocha,
  • Ana I. Pereira

摘要

This study delves into bio-inspired approaches and clustering methodologies to introduce an automated clustering algorithm named Multi-objective Clustering Algorithm (MCA). Using multi-objective strategies and several combination measures, this method calculates the optimal number of clusters and element partitioning by minimizing intra-clustering measures and maximizing inter-clustering ones. Through experimentation on three benchmark datasets, the results highlight the success of the MCA in obtaining a set of optimal solutions (Hybrid Pareto front) through the integration of multi-objective strategies and clustering measures. Moreover, the Dunn clustering validity index is used to support the decision maker in selecting the optimal solution among the ones presented in the Hybrid Pareto front. This approach allows decision-makers to choose the most suitable solution by incorporating additional insights beyond the model.