A Bee-Inspired Multiobjective Optimization Clustering Algorithm

Dávila P. F. Cruz, Alexandre A. Politi, Danilo Cunha, Leandro N. de Castro, and Renato D. Maia


clustering, multiobjective optimization, multiobjective clustering optimization, beeinspired 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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