Avaliação de métodos para determinação do número ótimo de clusters em estudo de divergência genética entre acessos de pimenta

• Ano de defesa: 2009
• Autor(a) principal: Faria, Priscila Neves
• Orientador(a): Cecon, Paulo Roberto
• Banca de defesa: Peternelli, Luiz Alexandre , Carneiro, Antônio Policarpo Souza , Finger, Fernando Luiz
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Viçosa
• Programa de Pós-Graduação: Mestrado em Estatística Aplicada e Biometria
• Departamento: Estatística Aplicada e Biometria
• País: BR
• Palavras-chave em Português: Análise de agrupamentos; Capsicum chinense; RMSSTD; RS; Método de máxima curvatura
• Palavras-chave em Inglês: Cluster analysis; Capsicum chinense; RMSSTD (Root Mean Square Standard Deviation); R Squared
• Área do conhecimento CNPq: CNPQ::CIENCIAS AGRARIAS
• Link de acesso: http://locus.ufv.br/handle/123456789/4018

Many times, the interpretation of the results in cluster analysis is done subjectively, that is, through inspection on dendograms, since there are no objective criteria to identify the formed clusters. In face of such a problem, the present study aimed to: (1) find out an objective way to achieve the cut-point (optimal number of clusters) in a dendogram in order to help on taking the right decision; (2) work out index concepts such as Root Mean Square Standard Deviation (RMSSTD) and R Squared (RS), explaining the contribution of each one of them in determining the optimal number of cluster; (3) method application, aiming to identify divergent accessions that will be used on improvement programs. An alternative solution for this problem is to use the RMSSTD and RS which are calculated according to the number of variables among and within the clusters formed, characterizing an objective way to determine the optimal number. Another solution is achieved by using the RS. Some morphological characteristics of the forty nine accessions of the species Capsicum chinense Jacq. from the Germplasm Bank of Vegetables of the Federal University of Viçosa (Banco de Germoplasma de Hortaliças da Universidade Federal de Viçosa, Minas Gerais Brazil) were analyzed by means of cluster analysis. The accessions were clustered based on the proposed techniques and an optimal number of clusters was achieved. The 49 accessions analyzed were classified into only seven clusters according to the graph of the RMSSTD versus the number of clusters and the graph of the RS versus the number of clusters.