This article aims to show how a cluster analysis can be carried out, through the technical hierarchy and the non-hierarchy in the rate of infected by Covid-19 of the Brazilian states through the numbers of infected states in order to identify a similarity between the states. through the numbers of infected, offering a counterpoint to the criterion used to analyze the number of infected in the states, based on the size of the population and comparing with their Human Development Index (HDI). public and free platforms called Coronavirus//Brasil and Atlas Brasil 2013 in relation to the 2010 HDI. For the cluster analysis, the Mahalanobins matrix was used with the hierarchical method, the simple link, complete, average, ward link and a non-hierarchical method using the K-means method were also applied, the coefficient of confenetic correlation to measure the degree of fit between the original similar matrices and the matrix resulting from the simplification provided by the clustering method. However, the method that best represents the data was found to be the complete linkage method. When grouping the states of both data, it took into account the similarity between the variables of the data and the correlation where it can be observed that the data are correlated.

This study aims to compare the performance of each method (hierarchical and non-hierarchical) of the grouping formed by several HDI from the 27 brazilian states, through the cluster analysis technique. As well as determining how many states there are in each formed group, to thus specify which technique best represents the data. Data from Atlas Brasil 2013 were used in relation to the 2010 HDI. For cluster analysis, the Mahalanobins matrix was used with the hierarchical method, from the data obtained, we applied the simple linkage methods, complete, average, ward liaison and a non-hierarchical method through the K-means method, the conphenetic correlation coefficient was also applied to measure the degree of fit between the original similar matrices and the resulting matrix of simplification provided by the grouping method. However, the method that best represents the data was the complete link. When grouping the states, the similarity between the HDI-R, HDI-L and HDI-S variables was considered this relationship formed similar groups between the connections from different regions of Brazil.

Modo de acesso: World Wide WebInclui bibliografia 1. Ciências Sociais 2. Sociedade. Serviço Social I. Título CDD-300O conteúdo dos artigos e seus dados em sua forma, correção e confiabilidade são de responsabilidade exclusiva dos seus respectivos autores.

The multivariate statistic has been used in divergence studies concerning plants species. Analysis of the similarity or distance among individuals is an important tool for population. This among aims to present the main show the main coefficients of similarity and dissimilarity and their properties and the importance of axioms for the complement of similarity for the methods in cluster analysis. We evaluated the changes caused by five different similarity coefficients in the group of 11 plots and 17 species. We tested the coefficients of Jaccard, Sorensen-Dice, Simple Agreement, Russel e Rao e Rogers e Tanimoto comparisons being made ​​between them by cophenetic correlations, Rand, adjusted Rand and stress between the distances obtained by the addition of these coefficients, and also by means of dendrograms (visual inspection), projection efficiency in a two-dimensional space and groups formed by the method of average linkage. The results showed that the use of different similarity coefficients caused few changes in the grouping of installments in groups, and the validation obtained between similar plots. Even though few changes in the structure of most different groups, these coefficients changed some relationships between plots with high similarity.