Dávila P. F. Cruz, Alexandre A. Politi, Danilo Cunha, Leandro N. de Castro, and Renato D. Maia
clustering, multiobjective optimization, multiobjective clustering optimization, bee-inspired algorithm
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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