Please use this identifier to cite or link to this item: http://www.repositorio.ufop.br/handle/123456789/12483
Title: Multi-objective approach for multiple clusters detection in data points events.
Authors: Bodevan, Emerson Cotta
Duczmal, Luiz Henrique
Duarte, Anderson Ribeiro
Silva, Pedro Henrique Lopes
Moreira, Gladston Juliano Prates
Keywords: Spatial scan statistic
Arbitrarily shaped spatial cluster
Multi-objective optimization
Issue Date: 2019
Citation: BODEVAN, 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.
Abstract: The 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.
URI: http://www.repositorio.ufop.br/handle/123456789/12483
metadata.dc.identifier.uri2: https://www.tandfonline.com/doi/abs/10.1080/03610918.2019.1667392?journalCode=lssp20
metadata.dc.identifier.doi: https://doi.org/10.1080/03610918.2019.1667392
ISSN: 1532-4141
Appears in Collections:DECOM - Artigos publicados em periódicos

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