Please use this identifier to cite or link to this item:
http://hdl.handle.net/1843/42873| Type: | Artigo de Evento |
| Title: | A bee-inspired multiobjective optimization clustering algorithm |
| Authors: | Dávila Patrícia Ferreira Cruz Alexandre Alberto Politi Danilo Cunha Leandro Nunes de Castro Renato Dourado Maia |
| Abstract: | 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. |
| Subject: | Algoritmos Cluster (Sistema de computador) |
| language: | eng |
| metadata.dc.publisher.country: | Brasil |
| Publisher: | Universidade Federal de Minas Gerais |
| Publisher Initials: | UFMG |
| metadata.dc.publisher.department: | ICA - INSTITUTO DE CIÊNCIAS AGRÁRIAS |
| Rights: | Acesso Restrito |
| metadata.dc.identifier.doi: | 10.2316/P.2016.841-035 |
| URI: | http://hdl.handle.net/1843/42873 |
| Issue Date: | Aug-2016 |
| metadata.dc.url.externa: | https://www.actapress.com/Abstract.aspx?paperId=456259 |
| metadata.dc.relation.ispartof: | Modelling, Simulation and Identification - MSI 2016 |
| Appears in Collections: | Artigo de Evento |
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