A Self-Organizing Map Clustering Approach to Support Territorial Zoning

被引：0

作者：

da Silva, Marcos A. S. ^{[1
]}

Barreto, Pedro V. de A. ^{[1
,2
]}

Matos, Leonardo N. ^{[2
]}

Miranda Junior, Gastao F. ^{[3
]}

Dompieri, Marcia H. G. ^{[4
]}

de Moura, Fabio R. ^{[5
]}

Resende, Fabricia K. S. ^{[2
]}

Novais, Paulo ^{[6
]}

Oliveira, Pedro ^{[7
]}

机构：

[1] Embrapa Coastal Tablelands, BR-49025370 Aracaju, SE, Brazil

[2] Univ Fed Sergipe, Dept Comp Sci, Sao Cristovao, SE, Brazil

[3] Univ Fed Sergipe, Dept Math, Sao Cristovao, SE, Brazil

[4] Embrapa Terr, BR-13070115 Campinas, SP, Brazil

[5] Univ Fed Sergipe, Dept Econ, Sao Cristovao, SE, Brazil

[6] Univ Minho, Dept Comp, Braga, Portugal

[7] Minho Univ, ALGORITMI Ctr, LASI, Braga, Portugal

来源：

PROGRESS IN PATTERN RECOGNITION, IMAGE ANALYSIS, COMPUTER VISION, AND APPLICATIONS, CIARP 2023, PT I | 2024年 / 14469卷

关键词：

Alto Taquari basin; Ordinal categorical data; Spatial Analysis;

D O I：

10.1007/978-3-031-49018-7_20

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：272 / 286

页数：15

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