Resumo

BELTRAN, Beatriz  e  VILARINO, Darnes. Survey of Overlapping Clustering Algorithms. Comp. y Sist. [online]. 2020, vol.24, n.2, pp.575-581.  Epub 04-Out-2021. ISSN 2007-9737.  https://doi.org/10.13053/cys-24-2-3391.

This paper is presented as a study of the overlapping clustering algorithms that have been developed in the last years, researchers have been working on these algorithms in different ways, in some cases they are based on widely known algorithms such as k-means and in others that work with heuristics or graphs. The need to work in clustering algorithms with overlap is due to the fact that currently there are many problems that require that the obtained groups be non-exclusive and for which it gives the guideline for this analysis. The algorithms included in this analysis are: ADditive CLUstering, Overlapping K-means, Dynamic Overlapping Clustering based on Relevance, Overlapping Clustering based on Density and Compactness, MCLC, A tree-based incremental overlapping clustering method, INDCLUS and Hybrid K-means.

Palavras-chave : Clustering algorithms; supervised classification; overlapping cluster.