A bee-inspired multiobjective optimization clustering algorithm
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Universidade Federal de Minas Gerais
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Multiobjective clustering techniques have been used to simultaneously consider several complementary aspects of clustering quality. They optimize more than one cluster validity index simultaneously, leading to high-quality re-sults, and have emerged as attractive and robust alternatives for clustering problems. This paper proposes a bee-inspired multiobjective optimization algorithm to solve data clustering problems. The algorithm was run for different datasets and the results obtained showed high quality clusters and diversity of solutions, whilst a suitable number of clusters was automatically determined.
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Algoritmos, Cluster (Sistema de computador)
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https://www.actapress.com/Abstract.aspx?paperId=456259