A Survey of Evolutionary Multi-Objective Clustering Approaches
- URL: http://arxiv.org/abs/2110.08100v1
- Date: Fri, 15 Oct 2021 13:53:12 GMT
- Title: A Survey of Evolutionary Multi-Objective Clustering Approaches
- Authors: Cristina Y. Morimoto, Aurora Pozo, and Marc\'ilio C. P. de Souto
- Abstract summary: We analyze the algorithms based on the features and components presented in the proposed general architecture of the evolutionary multi-objective clustering.
It is essential to observe these aspects besides specific clustering properties when designing new approaches or selecting/using the existing ones.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This article presents how the studies of the evolutionary multi-objective
clustering have been evolving over the years, based on a mapping of the indexed
articles in the ACM, IEEE, and Scopus. We present the most relevant approaches
considering the high impact journals and conferences to provide an overview of
this study field. We analyzed the algorithms based on the features and
components presented in the proposed general architecture of the evolutionary
multi-objective clustering. These algorithms were grouped considering common
clustering strategies and applications. Furthermore, issues regarding the
difficulty in defining appropriate clustering criteria applied to evolutionary
multi-objective clustering and the importance of the evolutionary process
evaluation to have a clear view of the optimization efficiency are discussed.
It is essential to observe these aspects besides specific clustering properties
when designing new approaches or selecting/using the existing ones. Finally, we
present other potential subjects of future research, in which this article can
contribute to newcomers or busy researchers who want to have a wide vision of
the field.
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