Multi-clustering via evolutionary multi-objective optimization

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Rui Wang, Shiming Lai, Guohua Wu, Lining Xing, Ling Wang, Hisao Ishibuchi

Published: 2018, Last Modified: 22 Jul 2025Inf. Sci. 2018EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: Highlights•The parallelism feature of evolutionary multi-objective optimization (EMO) can be used to search for multiple clustering results simultaneously.•An a posteriori method, EMO-KC, is proposed to identify an appropriate cluster number.•A transformation strategy is designed for the construction of bi-objective optimization problem.