• Refine Query
  • Source
  • Publication year
  • to
  • Language
  • 1
  • Tagged with
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Módulos computacionais para seleção de variáveis e Análise de agrupamento para definição de zonas de manejo / Computational modules for variable selection and cluster analysis for definition of management zones

Gavioli, Alan 17 February 2017 (has links)
Submitted by Neusa Fagundes (neusa.fagundes@unioeste.br) on 2017-09-18T14:32:46Z No. of bitstreams: 1 Alan_Gavioli2017.pdf: 4935513 bytes, checksum: 58816f2871fee27474b2fd5e511826af (MD5) / Made available in DSpace on 2017-09-18T14:32:46Z (GMT). No. of bitstreams: 1 Alan_Gavioli2017.pdf: 4935513 bytes, checksum: 58816f2871fee27474b2fd5e511826af (MD5) Previous issue date: 2017-02-17 / 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. / A seleção de variáveis e a análise de agrupamento de dados são atividades fundamentais para a definição de zonas de manejo (ZMs) de qualidade. Para executar essas duas atividades, existem diversos métodos propostos, que devido à sua complexidade precisam ser executados por meio da utilização de sistemas computacionais. Neste trabalho, avaliaramse 5 métodos de seleção de variáveis baseados em análise de correlação espacial, análise de componentes principais (ACP) e análise espacial multivariada baseada no índice de Moran e em ACP (MULTISPATI-PCA). Propôs-se um novo algoritmo de seleção de variáveis, denominado MPCA-SC, desenvolvido a partir da aplicação conjunta da análise de correlação espacial e de MULTISPATI-PCA. Avaliou-se a viabilidade de aplicação de 20 algoritmos de agrupamento de dados para a geração de ZMs: 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, median linkage, método de McQuitty (mcquitty), método de Ward, neural gas, partitioning around medoids, single linkage, spherical k-means e unsupervised fuzzy competitive learning. Apresentaram-se ainda dois módulos computacionais desenvolvidos para disponibilizar os métodos de seleção de variáveis e de agrupamento de dados para a definição de ZMs. As avaliações foram realizadas com dados obtidos entre os anos de 2010 e 2015 de três áreas agrícolas comerciais, localizadas no estado do Paraná, nas quais cultivaram-se milho e soja. Os experimentos efetuados para avaliar os 5 algoritmos de seleção de variáveis mostraram que o novo método MPCA-SC pode melhorar a qualidade de ZMs em diversos aspectos, mesmo obtendo-se resultados satisfatórios com os outros 4 algoritmos. Os experimentos de avaliação dos 20 métodos de agrupamento citados mostraram que 17 deles foram adequados para o delineamento de ZMs, com destaque para fanny e mcquitty. Por fim, concluiu-se que os dois módulos computacionais desenvolvidos possibilitaram a obtenção de ZMs de qualidade. Além disso, esses módulos constituem uma ferramenta computacional mais abrangente que outros softwares de uso gratuito, como FuzME, MZA e SDUM, em relação à diversidade de algoritmos disponibilizados para selecionar variáveis e agrupar dados.

Page generated in 0.0769 seconds