Módulos computacionais para seleção de variáveis e Análise de agrupamento para definição de zonas de manejo

Abstract

Two basic activities for the definition of quality management zones (MZs) are the variable selection task and the cluster analysis task. There are several methods proposed to execute them, but due to their complexity, they need to be made available by computer systems. In this study, 5 methods based on spatial correlation analysis, principal component analysis (PCA) and multivariate spatial analysis based on Moran’s index and PCA (MULTISPATI-PCA) were evaluated. A new variable selection algorithm, named MPCA-SC, based on the combined use of spatial correlation analysis and MULTISPATI-PCA, was proposed. The potential use of 20 clustering algorithms for the generation of MZs was evaluated: average linkage, bagged clustering, centroid linkage, clustering large applications, complete linkage, divisive analysis, fuzzy analysis clustering (fanny), fuzzy c-means, fuzzy c-shells, hard competitive learning, hybrid hierarchical clustering, k-means, McQuitty’s method (mcquitty), median linkage, neural gas, partitioning around medoids, single linkage, spherical k-means, unsupervised fuzzy competitive learning, and Ward’s method. Two computational modules developed to provide the variable selection and data clustering methods for definition of MZs were also presented. The evaluations were conducted with data obtained between 2010 and 2015 in three commercial agricultural areas, cultivated with soybean and corn, in the state of Paraná, Brazil. The experiments performed to evaluate the 5 variable selection algorithms showed that the new method MPCA-SC can improve the quality of MZs in several aspects, even obtaining satisfactory results with the other 4 algorithms. The evaluation experiments of the 20 clustering methods showed that 17 of them were suitable for the delineation of MZs, especially fanny and mcquitty. Finally, it was concluded that the two computational modules developed made it possible to obtain quality MZs. Furthermore, these modules constitute a more complete computer system than other free-to-use software such as FuzME, MZA, and SDUM, in terms of the diversity of variable selection and data clustering algorithms.