Automatic segmentation of the self-organizing map to support territorial zoning.
Automatic segmentation of the self-organizing map to support territorial zoning.
Author(s): BARRETO, P. V. DE A.; SILVA, M. A. S. da; MATOS, L. N.; MIRANDA JÚNIOR, G. F.; DOMPIERI, M. H. G.; MOURA, F. R. DE; RESENDE, F. K. S.
Summary: ABSTRACT: This paper proposes an algorithm for analyzing clusters of thematic maps with ordinal categorical classes to support territorial zoning. The proposed method combines the Self-Organizing Map with graph segmentation techniques for data clustering. The approach was evaluated with synthetic data and applied to the environmental zoning of the Alto Taquari basin, MS/MT. The results showed the ability of the algorithm to separate the data into unimodal differentiable groups. RESUMO: Este artigo propõe um algoritmo para análise de agrupamentos de mapas temáticos com classes categóricas ordinais para suporte ao zoneamento territorial. O método proposto combina o Mapa Auto Organizável com técnicas de segmentação de grafos para clusterização dos dados. A abordagem foi avaliada com dados sintéticos e aplicada no zoneamento ambiental da bacia do Alto Taquari, MS/MT. Os resultados mostraram a capacidade do algoritmo separar os dados em grupos diferenciáveis unimodais.
Publication year: 2023
Types of publication: Paper in annals and proceedings
Unit: Embrapa Territorial
Keywords: Artificial intelligence, Data clustering
Observation
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