A self-organizing map clustering approach to support territorial zoning

This work aims to evaluate three strategies for analyzing clusters of ordinal categorical data (thematic maps) to support the territorial zoning of the Alto Taquari basin, MS/MT. We evaluated a model-based method, another based on the segmentation of the multi-way contingency table, and the last one based on the transformation of ordinal data into intervals and subsequent analysis of clusters from a proposed method of segmentation of the Self-Organizing Map after the neural network training process. The results showed the adequacy of the methods based on the Self-Organizen Map and the segmentation of the contingency table, as these techniques generated unimodal clusters with distinguishable groups.Supported by National Council for Scientific and Technological Development –CNPq, Brazil, and by National Funds through the Portuguese funding agency, FCT -Fundação para a Ciência e a Tecnologia within project 2022.06822.PTDC. The work of Pedro Oliveira was also supported by the doctoral Grant PRT/BD/154311/2022 financed by the Portuguese Foundation for Science and Technology (FCT), and with funds from European Union, under MIT Portugal Progra