Survey of Overlapping Clustering Algorithms

Authors

  • Beatriz Beltrán Benemérita Universidad Autónoma de Puebla, Faculty of Computer Science
  • Darnes Vilariño Benemérita Universidad Autónoma de Puebla, Faculty of Computer Science

DOI:

https://doi.org/10.13053/cys-24-2-3391

Keywords:

Clustering algorithms, supervised classification, overlapping cluster

Abstract

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 orgraphs. 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 HybridK-means.

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Published

2020-06-23

Issue

Vol. 24 No. 2 (2020): Thematic Issue on Language & Knowledge Engineering (Guest editors: D. Pinto, B. Beltrán, A. Vázquez)

Section

Articles of the Thematic Issue

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