Abstract
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 Program.
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da Silva, M.A.S. et al. (2024). A Self-Organizing Map Clustering Approach to Support Territorial Zoning. In: Vasconcelos, V., Domingues, I., Paredes, S. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2023. Lecture Notes in Computer Science, vol 14469. Springer, Cham. https://doi.org/10.1007/978-3-031-49018-7_20
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