Multi-objective approach for multiple clusters detection in data points events.

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dc.contributor.authorBodevan, Emerson Cotta
dc.contributor.authorDuczmal, Luiz Henrique
dc.contributor.authorDuarte, Anderson Ribeiro
dc.contributor.authorSilva, Pedro Henrique Lopes
dc.contributor.authorMoreira, Gladston Juliano Prates
dc.date.accessioned2020-07-21T13:45:09Z
dc.date.available2020-07-21T13:45:09Z
dc.date.issued2019
dc.description.abstractThe spatial scan statistic is a widely used technique for detecting spatial clusters. Several extensions of this technique have been developed over the years. The objectives of these techniques are the detection accuracy improvement and a flexibilization on the search clusters space. Based on Voronoi-Based Scan (VBScan), we propose a biobjective approach using a recursively VBScan method called multiobjective multiple clusters VBScan (MOMC-VBScan), alongside a new measure called matching. This approach aims to identify and delineate all multiple significant anomalies in a search space. We conduct several experiments on different simulated maps and two real datasets, showing promising results. The proposed approach proved to be fast and with good precision in determining the partitions.pt_BR
dc.identifier.citationBODEVAN, E. C. et al. Multi-objective approach for multiple clusters detection in data points events. Communications In Statistics - Simulation and Computation, set. 2019. Disponível em: <https://www.tandfonline.com/doi/abs/10.1080/03610918.2019.1667392?journalCode=lssp20>. Acesso em: 18 jun. 2020.pt_BR
dc.identifier.doihttps://doi.org/10.1080/03610918.2019.1667392pt_BR
dc.identifier.issn1532-4141
dc.identifier.urihttp://www.repositorio.ufop.br/handle/123456789/12483
dc.identifier.uri2https://www.tandfonline.com/doi/abs/10.1080/03610918.2019.1667392?journalCode=lssp20pt_BR
dc.language.isoen_USpt_BR
dc.rightsrestritopt_BR
dc.subjectSpatial scan statisticpt_BR
dc.subjectArbitrarily shaped spatial clusterpt_BR
dc.subjectMulti-objective optimizationpt_BR
dc.titleMulti-objective approach for multiple clusters detection in data points events.pt_BR
dc.typeArtigo publicado em periodicopt_BR
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