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SCI."],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Complex real-world problems require advanced models for large datasets; combining optimization and machine learning methods can enhance solution effectiveness and efficiency. This work presents an automatic bio-inspired clustering algorithm named Multi-objective Clustering Algorithm II. Through an optimization process, the algorithm autonomously determines the number of clusters, their centroids, and the optimal distribution of their elements. Furthermore, the paper also presents a split and merge strategy for clustering algorithms, with a special focus on multi-objective ones. The proposed algorithms were executed on 10 benchmark datasets, yielding satisfactory results by accurately estimating the optimal number of clusters and providing appropriate dataset partitions. 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