Please use this identifier to cite or link to this item:
http://hdl.handle.net/1843/42910
Type: | Artigo de Periódico |
Title: | Bio-inspired multiobjective clustering optimization: a survey and a proposal |
Authors: | Danilo Cunha Dávila Cruz Alexandre Politi 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 two or more cluster validity indices simultaneously, they lead to high-quality results, and have emerged as attractive and robust alternatives for solving clustering problems. This paper provides a brief review of bio-Inspired multiobjective clustering, and proposes a bee-inspired multiobjective optimization (MOO) algorithm, named cOptBees-MO, to solve multiobjective data clustering problems. In its survey part, a brief tutorial on MOO and multiobjective clustering optimization (MOCO) is presented, followed by a review of the main works in the area. Particular attention is given to the many objective functions used in MOCO. To evaluate the performance of the algorithm it was executed for various datasets and the results presented high quality clusters, diverse solutions an the automatic determination of a suitable number of clusters. |
Subject: | Análise por agrupamento 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 Aberto |
metadata.dc.identifier.doi: | https://doi.org/10.5430/air.v6n2p10 |
URI: | http://hdl.handle.net/1843/42910 |
Issue Date: | 2017 |
metadata.dc.url.externa: | https://www.sciedu.ca/journal/index.php/air/article/view/10658 |
metadata.dc.relation.ispartof: | Artificial Intelligence Research |
Appears in Collections: | Artigo de Periódico |
Files in This Item:
File | Description | Size | Format | |
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Bio-inspired multiobjective clustering optimization a survey and a proposal.pdf | 1.05 MB | Adobe PDF | View/Open |
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